Search

Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field. • Summarize existing methods from a system perspective. • Introduce different methods in a "question–answer" paradigm. • Introduce software platforms and benchmark datasets. • Conduct an experimental study. [ABSTRACT FROM AUTHOR]

The low-rank representation (LRR) method has attracted widespread attention due to its excellent performance in pattern recognition and machine learning. LRR-based variants have been proposed to solve the three existing problems in LRR: (1) the projection matrix is permanently fixed when dimensionality reduction techniques are adopted; (2) LRR fails to capture the local geometric structure; and (3) the solution deviates from the real low-rank solution. To address these problems, this paper proposes a low-rank representation with projection distance regularization (PDRLRR) via manifold optimization for clustering. In detail, we first introduce a low-dimensional projection matrix and a projection distance regularization term to fit the projected data automatically and capture the local structure of the data, respectively. Consequently, the projection matrix and representation matrix are obtained jointly. Then, we obtain a more accurate low-rank solution by minimizing the Schatten- p norm instead of the nuclear norm. Next, the projection matrix is optimized through a generalized Stiefel manifold. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art methods. • This paper proposes a novel PDRLRR model that can simultaneously address the three common problems in LRR. • Data dimensionality reduction technology is integrated into LRR, reducing the data dimensions while learning representation matrices. • For extracting the complete information, the projection distance regularization term is introduced to capture the global and local structure of the data. • The Schatten- p norm instead of the nuclear norm is employed to solve the rank minimization problem of the representation matrix, which can more accurately approximate the real low-rank solution. [ABSTRACT FROM AUTHOR]

• Almost all these works above take the independent and identically distributed (i.i.d.) assumption, i.e., regarding all samples in various computer vision tasks as i.i.d., but they are not i.i.d. samples and the samples of many machine learning applications such as speech recognition, system diagnosis, and market forecasting are also proved to be non-i.i.d. samples. In particular, with the advent of the big data era, multi-class learning problems-such as text or image classification - can involve tens or hundreds of thousands of classes, and the samples are usually spatially-connected or temporally-correlated with their physically-connected neighbours, which can not be directly handled by these existing methods or algorithms. This implies that many samples are not satisfy the i.i.d. assumption or this assumption is very restrictive and can not be strictly justified in real-world problems. In addition, the AIO methods above are theoretically reasonable (i.e., Fisher consistent) and experiments indicate that they can lead to well-generalizing hypothesis, but efficient algorithms are lacking so far. Then three problems are posed: • Whether the AIO-MSVM algorithm based on non-i.i.d. samples is consistent? If this algorithm is consistent, what is the learning rate of this algorithm with non-i.i.d. samples? Whether the efficient AIO-MSVM algorithm can be proposed (or whether the AIO-MSVM algorithm with i.i.d. samples can be improved)? • The goal of this paper is to answer these problems above. Now, let' s highlight the main features of this paper. • The generalization bound of AIO-MSVM algorithm with u.e.M.c. samples is established and the fast learning rate is obtained. The AIO-MSVM algorithm with u.e.M.c. samples is proved to be consistent. To our knowledge, these studies are the first works on this topic. • A new algorithm, called AIO-MSVM algorithm based on q -times Markovian resampling (AIO-Mar- q), is proposed. The effectiveness and efficiency of the proposed algorithm are validated by experiments on public datasets. The generalization performance of "All-in-one" Multi-class SVM (AIO-MSVM) based on uniformly ergodic Markovian chain (u.e.M.c.) samples is considered. We establish the fast learning rate of AIO-MSVM algorithm with u.e.M.c. samples and prove that AIO-MSVM algorithm with u.e.M.c. samples is consistent. We also propose a novel AIO-MSVM algorithm based on q -times Markovian resampling (AIO-MSVM-MR), and show the numerical investigation on the learning performance of AIO-MSVM-MR based on public datasets. The experimental studies indicate that compared to the classical AIO-MSVM algorithm and other MSVM algorithms, the proposed AIO-MSVM-MR algorithm has not only smaller misclassification rate, but also less sampling and training total time. We present some discussions on the case of unbalanced training samples, the choices of q and two technical parameters, and present some explanations on the learning performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• The paper proposes the Presumably Correct Decision Sets for handling uncertainty. • Similarity neighborhoods define weak, borderline and strong members of each class. • Presumably correct and incorrect sets allow detecting difficult instances. • Numerical simulations illustrate the advantages of our model compared to rough sets. The paper presents the presumably correct decision sets as a tool to analyze uncertainty in the form of inconsistency in decision systems. As a first step, problem instances are gathered into three regions containing weak members, borderline members, and strong members. This is accomplished by using the membership degrees of instances to their neighborhoods while neglecting their actual labels. As a second step, we derive the presumably correct and incorrect sets by contrasting the decision classes determined by a neighborhood function with the actual decision classes. We extract these sets from either the regions containing strong members or the whole universe, which defines the strict and relaxed versions of our theoretical formalism. These sets allow isolating the instances difficult to handle by machine learning algorithms as they are responsible for inconsistent patterns. The simulations using synthetic and real-world datasets illustrate the advantages of our model compared to rough sets, which is deemed a solid state-of-the-art approach to cope with inconsistency. In particular, it is shown that we can increase the accuracy of selected classifiers up to 36% by weighting the presumably correct and incorrect instances during the training process. [ABSTRACT FROM AUTHOR]

Extensive research has been conducted on analyzing human movements, driven by its diverse practical applications such as human–robot interaction, human learning, and clinical diagnosis. However, the current state-of-the-art still encounters scientific challenges when it comes to modeling human movements. There are two key aspects that need to be addressed. Firstly, new models should consider the stochastic nature of human movement and the physical structure of the human body to accurately predict the patterns in full-body motion descriptors over time. Secondly, while deep learning algorithms have been utilized, they lack explainability in terms of predicting body posture sequences, making it essential to improve their comprehensible representation of human movement. This paper aims to tackle these challenges by presenting three innovative methods for creating explainable representations of human movement. The study formulates human body movement as a state-space model based on the Gesture Operational Model (GOM). Model parameters are estimated through either one-shot training employing Kalman Filters or data-intensive training utilizing artificial neural networks. The trained models are utilized for analyzing the dexterity of expert professionals in full-body movements, enabling the identification of dynamic associations between body joints and gesture recognition. Additionally, these models are employed to generate artificial professional movements. • Three innovative methods for explainable human movement models. • Body dexterity analysis: Models reveal how artisans and operators perform tasks. • Minimal motion descriptors to enhance task recognition. • Artificial generation of full-body professional movements. • Extensive experimentation with real-world datasets. [ABSTRACT FROM AUTHOR]

This paper addresses a machine learning problem often challenged by differences in the distributions of training and real-world data. We propose a framework that addresses the problem of underfitting in the ensembling method using pre-trained models and improves the performance and robustness of deep learning models through ensemble diversity. For the naive weight ensembling framework, we discovered that the ensembled models could not lie in the same loss basin under extreme domain shift conditions, suggesting that a loss barrier may exist. We used a fine-tuning step after the weighted ensemble to address the underfitting problem caused by the loss barrier and stabilize the batch normalization running parameters. We also inferred through qualitative analysis that the diversity of ensemble models affects domain generalization. We validate our method on a large-scale image dataset (ImageNet-1K) and chemical molecule data, which is suitable for testing with domain shift problems due to its data-splitting method. • We generalized the model via weight ensembling under an extreme distribution shift. • Our framework solves the underfitting problem of ensembles of pre-trained models. • We achieved up to 16.3% AUROC improvement over the baseline on the benchmark dataset. • Our model settles into a stable minima, which we verify on the loss surface. • We analyzed the correlation between model diversity and domain generalization. [ABSTRACT FROM AUTHOR]

Graph meta-learning has recently received significantly increased attention by virtue of its potential to extract common and transferable knowledge from learning different tasks on a graph. Existing methods for graph meta-learning usually leverage local subgraphs to transfer subgraph-specific information. However, they inherently face the challenge of imbalanced subgraphs due to inconsistent node density and different label distributions over local subgraphs. This paper proposes an adaptive graph meta-learning framework (AG-Meta) for learning the consistent and transferable representation of a graph in a way that can adapt to imbalanced subgraphs. Specifically, AG-Meta first learns the structural representation of subgraphs with various degrees using an Adaptive Graph Cascade Diffusion Network (AGCDN). AG-Meta then employs a prototype-consistency classifier to produce more accurate transferable inductive representations (also called prototypes) under few-shot settings with different label distributions of a subgraph. In the context of optimizing a model-agnostic meta-learner, a novel metric loss is finally introduced to achieve structural representation and prototype consistency. Extensive experiments are conducted to compare AG-Meta against baselines on five real-world networks, which validates that AG-Meta outperforms the state-of-the-art approaches. • A novel adaptive graph meta-learning framework is proposed. • An ensemble weight scheme is used to deal with inconsistency in densities and labels. • A prototypical-consistency loss is developed for graph meta-learning. • Our AG-Meta model outperforms several baselines. [ABSTRACT FROM AUTHOR]

Active learning alleviates labeling costs by selecting and labeling the most informative examples from an unlabeled pool. However, most existing active learning approaches estimate informativeness with uncalibrated confidence, resulting in unreliable informativeness estimation. These approaches generally ignored two significant issues caused by uncalibrated confidence methods. Firstly, the average uncalibrated confidence generated by modern neural networks is usually higher than the accuracy. Secondly, examples located near the decision boundaries are unstable during prediction when the target model updates parameters in the last several epochs, even throughout the training process. This phenomenon, caused by the forgetting characteristic of neural networks, has a significant impact on some specific models that estimate the informativeness by predicted probability vectors or pseudo labels. To address these issues, in this paper, we propose a novel active learning approach to reliably estimate informativeness with calibrated confidence. Specifically, we integrate the intermediate predictions for each unlabeled example, generated by the target model during the training process, to generate calibrated confidence. The calibrated confidence can capture a tendentious label from an indecisive subset of the class space. We show that the calibrated confidence with tendentiousness can maintain the ability of correct predictions. The empirical results demonstrate that our approach outperforms the state-of-the-art active learning methods on image classification tasks. • The active learning algorithm selects unstable examples by calibrated confidence.. • The confidence calibration method only requires training a single model. • Extensive experiments show the competitive performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Lifelong learning, the ability to continually learn new concepts throughout our life, is a hallmark of human intelligence. Generally, humans learn a new concept by knowing what it looks like and what makes it different from the others , which are correlated. Those two ways can be characterized by generation and classification in machine learning respectively. In this paper, we carefully design a dynamically growing GAN called Introspective GAN (IntroGAN) that can perform incremental generation and classification simultaneously with the guidance of prototypes, inspired by their roles of efficient information organization in human visual learning and excellent performance in other fields like zero-shot/few-shot/incremental learning. Specifically, we incorporate prototype-based classification which is robust to feature change in incremental learning and GAN as a generative memory to alleviate forgetting into a unified end-to-end framework. A comprehensive benchmark on the joint incremental generation and classification task is proposed and our method demonstrates promising results. Additionally, we conduct comprehensive analyses over the properties of IntroGAN and verify that generation and classification can be mutually beneficial in incremental scenarios, which is an inspiring area to be further exploited. The code is available at https://github.com/TonyPod/IntroGAN. • Propose a benchmark to evaluate joint incremental generation and classification. • Propose an IntroGAN to address the joint incremental and classification task. • Introduce a fine-tuning technique that can accelerate incremental GAN training. • Superior performance on proposed benchmarks and more challenging scenarios. [ABSTRACT FROM AUTHOR]

• Cover all FSL papers from the 2000s to now, elaborate its evolution history. • Give an understandable hierarchical taxonomy for FSL approaches. • Categorize meta learning FSL methods and reveal their underlying relationship. • Analyze several emerging extensional research hotspots of FSL. • Conclude existing FSL applications and its future directions. The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence. Despite the long history dated back to the early 2000s and the widespread attention in recent years with booming deep learning, few surveys for few sample learning (FSL) are available. We extensively study almost all papers of FSL spanning from the 2000s to now and provide a timely and comprehensive survey for FSL. In this survey, we review the evolution history and current progress on FSL, categorize FSL approaches into the generative model based and discriminative model based kinds in principle, and emphasize particularly on the meta learning based FSL approaches. We also summarize several recently emerging extensional topics of FSL and review their latest advances. Furthermore, we highlight the important FSL applications covering many research hotspots in computer vision, natural language processing, audio and speech, reinforcement learning and robotic, data analysis, etc. Finally, we conclude the survey with a discussion on promising trends in the hope of providing guidance and insights to follow-up researches. [ABSTRACT FROM AUTHOR]

• We propose a novel Self-taught Multi-view Spectral Clustering (SMSC) framework, which can directly obtain a discrete indicator matrix. • Our SMSC is devised to integrate adaptive weighting, Laplacian embedding (spectral embedding), consensus graph learning, and discrete indicator matrix learning into a unified framework. • Our SMSC can dynamically update the consensus graph based on the feedback of the discrete cluster label matrix and vice versa. • An efficient algorithm is proposed to solve the challenging discrete optimization problem alternately. By integrating multiple views, i.e., multi-view learning (ML), we can discover the underlying data structures so that the performance of learning tasks can improve. As a basic and important branch of ML, multi-view clustering has achieved great success recently in pattern recognition and machine learning communities. Most existing multi-view spectral clustering methods heavily adopt the relax-and-discretize strategy to obtain discrete cluster labels (clustering results), i.e., using predefined similarity graphs to learn a consensus Laplacian embedding shared by all views for K -means clustering. However, the above clustering strategy may significantly affect clustering performance since there is information loss between independent steps. In this paper, we establish a novel Self-taught Multi-view Spectral Clustering (SMSC) framework to address the above issue. As the main contributions of this paper, we provide two versions of SMSC based on convex combination and centroid graph fusion schemes. Specifically, a self-taught mechanism is introduced in SMSC, which can effectively feedback the manifold structure induced by Laplacian embedding and the cluster information hidden in the discrete indicator matrix to learn an optimal consensus similarity graph for graph partitioning. The effectiveness of the proposed methods has been evaluated on real-world multi-view datasets, and experimental results show that our methods outperform other state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

• The proposed dual-branch network separates knowledge to avoid gradient conflicts. • The proposed adaptive feature fusion module aggregates knowledge from two branches. • The proposed model compression stage preserves the dual-branch structure. • Experiments show that our method outperforms other methods on different benchmarks. Most existing class incremental learning methods rely on storing old exemplars to avoid catastrophic forgetting. However, these methods inevitably face the gradient conflict problem, the inherent conflict between new streaming knowledge and existing knowledge in the gradient direction. To alleviate gradient conflict, this paper reuses the previous knowledge and expands the branch to accommodate new concepts instead of fine-tuning the original models. Specifically, this paper designs a novel dual-branch network called Knowledge Aggregation Networks. The previously trained model is frozen as a branch to retain existing knowledge, and a consistent trainable network is constructed as the other branch to learn new concepts. An adaptive feature fusion module is adopted to dynamically balance the two branches' information during training. Moreover, a model compression stage maintains the dual-branch structure. Extensive experiments on CIFAR-100, ImageNet-Sub, and ImageNet show that our method significantly outperforms the other methods and effectively balances stability and plasticity. [ABSTRACT FROM AUTHOR]

Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. In this paper, we propose a new graph neural network architecture that substitutes classical message passing with an analysis of the local distribution of node features. To this end, we extract the distribution of features in the egonet for each local neighbourhood and compare them against a set of learned label distributions by taking the histogram intersection kernel. The similarity information is then propagated to other nodes in the network, effectively creating a message passing-like mechanism where the message is determined by the ensemble of the features. We perform an ablation study to evaluate the network's performance under different choices of its hyper-parameters. Finally, we test our model on standard graph classification and regression benchmarks, and we find that it outperforms widely used alternative approaches, including both graph kernels and graph neural networks. • We propose a novel GNN that analyzes local distributions of node features. • These are compared against learned ones using the histogram intersection kernel. • The similarity information is propagated to other nodes in the network. • This creates a message passing mechanism where the aggregation is non-linear. • Extensive experimental results demonstrate the competitiveness of our model. [ABSTRACT FROM AUTHOR]

Underwater images are usually covered with a blue–greenish colour cast, making them distorted, blurry or low in contrast. This phenomenon occurs due to the light attenuation given by the scattering and absorption in the water column. In this paper we present an image dewatering approach motivated upon the observation that the image formation model can be used to drive the learning process by constraining the loss function and making used of paired data. To this end, we employ a conditional generative adversarial network (cGAN) with two generators. Our Dual Generator Dewatering cGAN (DGD-cGAN) removes the haze and colour cast induced by the water column and restores the true colours of underwater scenes whereby the effects of various attenuation and scattering phenomena that occur in underwater images are tackled by the two generators. The first generator takes as input the underwater image and predicts the dewatered scene, while the second generator learns the underwater image formation process by implementing a custom loss function based upon the transmission and the veiling light components of the image formation model. Extensive experiments show that DGD-cGAN consistently delivers a margin of improvement as compared with state-of-the-art methods on several widely available datasets. • A dual generator conditional adversarial network performing underwater image restoration. • Constraining the colour restoration by the underwater image formation model. • Adversarial network training on a small dataset of real-world underwater images. • Implementing machine learning to cope with light attenuation and scattering. [ABSTRACT FROM AUTHOR]

Causal discovery reveals the true causal relationships behind data and discovering causal relationships from observed data is a particularly challenging problem, especially in large-scale datasets. The functional causal model is an effective method for causal discovery, but its time efficiency cannot be guaranteed. How to efficiently apply it to massive data still needs to be solved. In this paper, we propose a coreset construction for the additive noise model to accelerate causal discovery. According to the asymmetry characteristic of causality, samples were assigned different weights to construct the coreset. With the constructed coreset, we propose a Fast causal discovery algorithm based on the Additive Noise Model (FANM) to improve the time efficiency of the functional causal model while ensuring the result performance of causal discovery. Experiments on synthetic data and real-world data show that our proposed algorithm is much more time-efficient than the methods based on the functional causal model, and the runtime of FANM remains consistent as sample size increases while maintaining or exceeding the accuracy of the original nonlinear additive noise model. • New coresets proposed for the additive noise model greatly reduces the data size for causal discovery. • A time-efficient algorithm, FANM, is proposed for causal discovery based on the coresets. • The coreset construction is applied to causal graph learning algorithm, CDCSG, to show its effectiveness. • The proposed FANM is verified by both synthetic data and real-world data. [ABSTRACT FROM AUTHOR]

Domain adaptation enables effective transfer between source and target domains with different distributions. The latest research focuses on open set domain adaptation; that is, the target domain contains unknown categories that do not exist in the source domain. The existing open set domain adaptation cannot realize the fine-grained recognition of unknown categories. In this paper, we propose an uncertainty analysis evidence model and design a distribution driven active transfer learning (DATL) algorithm. DATL realizes fine-grained recognition of unknown categories with no requirements on the source domain to contain the unknown categories. To explore unknown distributions, the uncertainty analysis evidence model was adopted to divide the high uncertainty space. To select critical instances, a cluster-diversity query strategy was proposed to identify new categories. To enrich the label categories of the source domain, a global dynamic alignment strategy was designed to avoid negative transfers. Comparative experiments with state-of-the-art methods on the standard Office-31/Office-Home/Office-Caltech10 benchmarks showed that the DATL algorithm: (1) outperformed its competitors; (2) realized accurate identification of unknown subcategories from a fine-grained perspective; and (3) achieved outstanding performance even with a very high degree of openness. • We propose a fine-grained open set domain adaptation issue. • We design a distribution driven active transfer learning algorithm. • We evaluate our method on benchmark datasets and demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

With the emerging of new data collection ways, the features are incremental and accumulated gradually. Due to the expansion of feature spaces, it is more common that there are unknown biases between the distribution of training and testing datasets. It is known as the unknown data selection bias, which belongs to the learning scenario with non-i.i.d samples. The performance of traditional approaches, which need the i.i.d. assumption, will be aggravated seriously. How to design an algorithm to address the problem of data selection bias in this feature incremental scenario is crucial but rarely studied. In this paper, we propose a feature incremental classification algorithm with causality. Firstly, we embed the confounding variable balance algorithm in causal learning into the prediction modeling and utilize the logical regression algorithm with balancing regular terms as a baseline. Then, to satisfy the special requirement of feature increment, we design a new regularizer, which maintains the consistency of the regression coefficients between the data in the current and previous stages. It retains the correlation between the old features and labels. Finally, we propose the Multiple Balancing Logistic Regression model (MBRLR) to jointly optimize the balancing regularizer and weighted logistic regression model with multiple feature sets. We also present theoretical results to show that our proposed algorithm can make precise and stable predictions. Besides, the numerical results also demonstrate that our MBRLR algorithm is superior to other methods. • We have pioneered the use of causal inference and proposed a novel method approach, i.e., MBRLR, for achieving stable and accurate classification in the feature increase scenario. • We have improved the sample reweighting method by redesigning the loss function and regularizer which could be formulated as a convex optimization problem and could be easily solved. • We have proved that the MBRLR algorithm can make a stable classification for feature incremental data in the non-i.i.d case. [ABSTRACT FROM AUTHOR]

• This paper proposes a method to replace the linear mapping-based spectral analysis with neural networks, which is more appropriate to learn the nonlinear mapping relationship between the original data and the pseudo cluster label. • Through constructing two different adaptive graph constraints, the mutual interaction between the NNs-based pseudo label learning module and the self-expression module is facilitated to explore the feature selection priority for the raw data. • Through a lot of well-designed experiments, the effectiveness of the method proposed in this paper has been fully proven. Unsupervised feature selection (UFS), which selects the most important feature subset and eliminates the unnecessary information for the upcoming data analysis, is a significant problem in machine learning and has been explored for years. Most UFS methods map features into a pseudo label space by multiplying a projection matrix constrained with sparsity to learn the mapping from the features to the labels. However, the mapping relationship is usually not linear, and linear regression may result in a suboptimal selection. To address this issue, we propose a novel UFS method, called neural networks embedded self-expression (NNSE). NNSE replaces the linear regression of traditional spectral analysis methods with neural networks to learn the pseudo label space. Besides, we embed neural networks into the self-expression model to improve the representative ability by preserving the local structure with an adaptive graph regularization module. Then we propose an efficient alternative iterative algorithm to solve the proposed model. Experimental results on 8 public datasets show NNSE outperforms the other state-of-the-art methods. Moreover, experimental results are also presented to show the convergence of the proposed method. The source code is available at: https://github.com/misteru/NNSE. [ABSTRACT FROM AUTHOR]

• The first attempt to design a GEP-based codematrix generation algorithm. The individual structure of GEP allows the codematrix to be optimized in a much faster and more effective way. • The feature selection scheme is combined with the design of codematrix, to accomplish the feature selection and codematrix optimization simultaneously. • A greedy strategy-based adaptive optimization method to further improve the individual's quality to deal with hard classes by appending proper columns to the codematrix. The multi-class classification task is one of the most common tasks in machine learning. As a typical solution based on a partitioning strategy, Error-Correcting Output Codes (ECOC) can transform a multi-class classification problem into multiple binary classification problems. The key of ECOC is to construct an effective codematrix to represent a set of class decomposition schemes, which transforms a multiclass problem into a group of binary class problems. Consequently, the design of a fast and effective ECOC codematrix generation method is of great research significance and value for solving multi-class classification problems. In ECOC algorithms, the design of codematrix is treated as a combination problem between different code columns, in which the evolutionary algorithm shows a great advantage. Based on this consideration, the Gene Expression Programming (GEP) is applied to search for the codematrix with high performance because its expressive tree structure makes it well represent codematrcies for subsequent optimization operations. This paper proposes an adaptive ECOC algorithm based on Gene Expression Programming (GEP) and similarity measurement matrix, named GEP-ECOC. In our GEP, each individual represents a set of columns to form a random ECOC codematrix, which is optimized in the evolutionary process. Meanwhile, the crossover and mutation operations are modified to include a legality checking process to ensure that the generated codematrix satisfies the ECOC constraints. The GEP-based ECOC codematrix generation algorithm can quickly produce a codematrix with better performance, which ensures the efficiency of the algorithm to a certain extent. In addition, an adaptive algorithm based on a similarity measurement matrix is proposed to add new columns to the current codematrix, aiming to better handle hard classes. Our algorithm is compared with other algorithms on various data sets, and the experimental results confirm that our GEP-ECOC can balance the efficiency and performance of the algorithm and achieve higher performance. [ABSTRACT FROM AUTHOR]

In this paper, we develop an approach to exploit kernel methods with data lying on the m -D Kendall shape space. When data arise in a finite-dimensional curved Riemannian manifold, as in this case, the usual Euclidean computer vision and machine learning algorithms must be treated carefully. A good approach is to use positive definite kernels on manifolds to embed the manifold with its corresponding metric in a high-dimensional reproducing kernel Hilbert space, where it is possible to utilize algorithms developed for linear spaces. Different Gaussian kernels can be found in the literature on the 2-D Kendall shape space to perform this embedding. The main novelty of this work is to provide a Gaussian kernel for the m -D Kendall shape space. This new Kernel coincides in the case m = 2 with the Gaussian kernels most widely used in the Kendall planar shape space and allows to define an embedding of the m -D Kendall shape space into a reproducible kernel Hilbert space. As far as we know, the complexity of the m -D Kendall shape space has meant that this embedding has not been addressed in the literature until now. This methodology will be tested on a machine learning problem with a simulated and a real data set. • Proposed kernels in the m-dimensional Kendalls space. • Shapes embedded in a high-dimensional Reproducing Kernel Hilbert Space. • Unification and generalization of Gaussian kernels defined in the space of planar shapes. • Supervised classification problems using a support vector machine algorithm. [ABSTRACT FROM AUTHOR]

Medical data sharing across institutes is crucial to large-scale multi-center studies and the development of real-world AI applications but suffers from serious privacy issues. A promising solution to address this challenge is federated learning, which typically aggregates a global model from heterogeneous data spread across numerous clients without exchanging data. However, the traditional federated learning algorithm (i.e. , FedAvg) merely aggregates the locally distributed models according to the amount of data on each client and lacks the consideration of data heterogeneity. In this paper, we propose a novel Federated Adaptive Reweighting (FedAR) algorithm for medical image classification. FedAR employs a flexible re-weighting scheme that can balance adaptively the contributions of the amount of data and the performance of the local model on each client to the weight of that client. Specifically, we allow the amount of local data to contribute more to the weight of each client in the early training stage and let the performance of the local model play a more important role in the late stage. We have evaluated the proposed FedAR algorithm against the locally trained model, globally trained baseline, and two existing federated learning algorithms on the ISIC2018 dataset and Chest X-ray14 dataset under the settings with a variable number of clients. Our results suggest that FedAR is an effective federated learning algorithm that substantially outperforms existing federated learning approaches. • Propose FedAR, based on federated learning, for medical image classification. • Develop an adaptive reweighting scheme to address the heterogeneity issue. • Demonstrate the effectiveness of FedAR on the ISIC2018 and Chest X-ray14 datasets. • FedAR improves the federated learning with limited extra communication costs. [ABSTRACT FROM AUTHOR]

Online portfolio selection (OPS) has received increasing attention from machine learning and quantitative finance communities. Despite their effectiveness, the pioneering OPS methods have several key limitations. First, price predictions are usually based on predetermined trends, which is inadequate for a fast-changing market patterns. Second, each asset is treated individually, ignoring the pervading relevance among the assets. Third, the risk terms are usually missing or inappropriate in optimizations. This paper proposes a novel OPS method, namely, the online low-dimension ensemble method, to overcome the limitations. Motivated by the stylized facts for the co-movements of assets, the financial market is regarded as a high-dimensional dynamical system (HDS), and a large number of low-dimensional subsystems (LDSs) are randomly generated from the HDS to extract the correlation information among the assets. The assets' price predictions are first made using these LDSs and then aggregated to formulate the final prediction using ensemble learning techniques. Thanks to the particular merits brought by our predicting scheme, we also develop a novel high-dimensional covariance matrix estimation/prediction method for short-term data, efficiently assessing the instantaneous risk of the projected portfolios. Compared with state-of-the-art methods, our approach obtains more accurate predictions as the correlation information is fully exploited. With the predictive instantaneous risk assessment, a more appropriate optimization problem is proposed, substantially improving the OPS setting and leading to significantly better investment performance. Therefore, this study develops a flexible and promising approach to learning fast-changing market patterns and demonstrates that the high-dimensional feature of the market is a crucial information source for financial modeling with short-term data rather than a barrier in the conventional sense. Extensive experiments on real-world datasets are conducted to illustrate our method further. • Proposing a novel approach to learn short-term high-dimensional pattern. • Developing a novel high-dimensional covariance matrix prediction/estimation method. • Substantially improving the optimization settings of OPS. [ABSTRACT FROM AUTHOR]

• The study uses cutting-edge deep learning algorithms for recognizing facial asymmetry, a key stroke symptom. • Our method significantly improves existing stroke detection techniques by providing automated, real-time assessment of potential stroke victims. • The research incorporates the latest findings in the field of pattern recognition, ensuring that our system is current and relevant. • We present a unique system that does not require any specialized equipment for detection, potentially improving the accessibility of stroke detection. • The paper demonstrates how this automated detection system could be a crucial first step in stroke segmentation, thus contributing to the early intervention and treatment of stroke. Strong efforts have been undertaken to enhance the diagnosis and identification of diseases that cause facial paralysis, such as Bell's palsy and stroke, because of their detrimental social effects. Stroke is one of the most serious and potentially fatal conditions among the major cardiovascular disorders. We are introducing a deep-learning-based method for early diagnosis of facial paralysis diseases such as stroke and Bell's palsy. Recognizing the costs associated with traditional diagnostic techniques like magnetic resonance tomography (MRI) and computed tomography (CT) scan images, our model employs a multi-task network, integrating face parsing, facial asymmetry parsing, and category enhancement. Spatial inconsistencies are addressed via a depth-map estimation module that leverages an instance-specific kernel approach. To clarify the boundaries of facial components, we use category edge detection with a foreground attention module, generating generic geometric structures and detailed semantic cues. Our model is trained on two datasets, comprising individuals with regular smiles and those with one-sided facial weakness. This cost-effective, easily accessible solution can streamline the diagnostic process, minimizing data gaps, and reducing needless rescreening and intervention costs. [ABSTRACT FROM AUTHOR]

• We propose a novel concept of Paired Contrastive Feature (PCF) for highly reliable writer-independent signature verification by converting a task with pairwise contrastive evaluation into a task with sample-wise evaluation. • Two highly reliable machine-learning frameworks, top-rank learning and learning with rejection, are applied to the writer-independent signature verification task for the first time by constructing PCF to the authors' best knowledge. • We validated the reliability and effectiveness of the proposed PCF with highly reliable methods through experiments with multiple evaluation metrics. [Display omitted] Signature verification requires high reliability. Especially in the writer-independent scenario with the skilled-forgery-only condition, achieving high reliability is challenging but very important. In this paper, we propose to apply two machine learning frameworks, learning with rejection and top-rank learning, to this task because they can suppress ambiguous results and thus give only reliable verification results. Since those frameworks accept a single input, we transform a pair of genuine and query signatures into a single feature vector, called Paired Contrastive Feature (PCF). PCF internally represents similarity (or discrepancy) between the two paired signatures; thus, reliable machine learning frameworks can make reliable decisions using PCF. Through experiments on three public signature datasets in the offline skilled-forgery-only writer-independent scenario, we evaluate and validate the effectiveness and reliability of the proposed models by comparing their performance with a state-of-the-art model. [ABSTRACT FROM AUTHOR]

• A fast and privacy-preserving training algorithm for deep autoencoders is proposed. • The encoder block of the autoencoder is trained using Singular Value Decomposition. • The decoder block of the autoencoder is trained layer by layer. • The non-iterative nature of the proposed training algorithm makes it faster than other approaches. • The method is especially suitable for federated learning and edge computing environments. This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (Deep AutoEncoder for Federated learning), unlike traditional neural networks, trains a deep autoencoder network in a non-iterative way, which drastically reduces training time. Training can be performed incrementally, in parallel and distributed and, thanks to its mathematical formulation, the information to be exchanged does not endanger the privacy of the training data. The method has been evaluated and compared with other state-of-the-art autoencoders, showing interesting results in terms of accuracy, speed and use of available resources. This makes DAEF a valid method for edge computing and federated learning, in addition to other classic machine learning scenarios. [ABSTRACT FROM AUTHOR]

• We propose a federated contrastive learning method, which uses the feature representation learned by the global model and the local model for contrastive learning, so that the final model can gradually approach the global model in the training process to obtain better model generalization. • In view of the current problem of data heterogeneity in real medical scenes, our method effectively reduces its impact on experimental results, that is, it can show high performance and generalization in the scenario of data heterogeneity. • We conducted experiments on two medical image classification datasets, i.e., skin lesion classification dataset for melanoma and chest X-ray dataset for detecting COVID-19. Extensive experiments demonstrate the efficacy of our approach. Federated learning, which allows distributed medical institutions to train a shared deep learning model with privacy protection, has become increasingly popular recently. However, in practical application, due to data heterogeneity between different hospitals, the performance of the model will be degraded in the training process. In this paper, we propose a federated contrastive learning (FedCL) approach. FedCL integrates the idea of contrastive learning into the federated learning framework. Specifically, it combines the local model and the global model for contrastive learning, so that the local model gradually approaches the global model with the increase of communication rounds, which improves the generalization ability of the model. We validate our method on two public datasets. Extensive experiments show that our method is superior to other federated learning algorithms in medical image classification. [ABSTRACT FROM AUTHOR]

• We propose a general-purpose nonlinear dimensionality reduction method named Vec2vec by extending the representation learning of words to numeric matrices, which has lower computational complexity than most existing dimensionality reduction methods on high-dimensional data. • We design a tailored objective function to preserve the pairwise similarities of data points and define the contexts of data points as random walk sequences in the neighborhood similarity graph, which serve as samples for model training. • We propose a more lightweight adaption of Vec2vec , which is referred to as AVec2vec. AVec2vec further reduces the computational complexity of Vec2vec with marginal loss in structural information. • We conduct extensive experiments for data classification and clustering on nine popular real-world image and text datasets to evaluate the performance of our method. The results show that Vec2vec outperforms several state-of-the-art dimensionality reduction methods in statistical hypothesis tests, and is more efficient than UMAP on high-dimensional data. The results also show that AVec2vec needs less computational time than Vec2vec with little performance loss in data classification and clustering tasks. Dimensionality reduction has many applications in data visualization and machine learning. Existing methods can be classified into global ones and local ones. The global methods usually learn the linear relationship in data, while the local ones learn the manifold intrinsic geometry structure, which has a significant impact on pattern recognition. However, most of existing local methods obtain an embedding with eigenvalue or singular value decomposition, where the computational complexities are very high in a large amount of high-dimensional data. In this paper, we propose a local nonlinear dimensionality reduction method named Vec2vec , which employs a neural network with only one hidden layer to reduce the computational complexity. We first build a neighborhood similarity graph from the input matrix, and then define the context of data points with the random walk properties in the graph. Finally, we train the neural network with the context of data points to learn the embedding of the matrix. We conduct extensive experiments of data classification and clustering on nine image and text datasets to evaluate the performance of our method. Experimental results show that Vec2vec is better than several state-of-the-art dimensionality reduction methods, except that it is equivalent to UMAP on data clustering tasks in the statistical hypothesis tests, but Vec2vec needs less computational time than UMAP in high-dimensional data. Furthermore, we propose a more lightweight method named Approximate Vec2vec (AVec2vec) with little performance degradation, which employs an approximate method to build the neighborhood similarity graph. AVec2vec is still better than some state-of-the-art local dimensionality reduction methods and competitive with UMAP on data classification and clustering tasks in the statistical hypothesis tests. [ABSTRACT FROM AUTHOR]

• Almost all studies about rule extraction investigate classification. In this paper, we study rule extraction from opaque predictive regression models. • Today, all black-box rule extraction methods suffer from potentially low fidelity on test data. By utilizing conformal prediction in a novel way, the fidelity can be guaranteed, thus solving the main problem with black-box rule extraction. • Another problem with rule extraction for regression is the choice of representation language; a standard regression tree with point predictions in the leaves is typically both too weak and convey very little information, while more complex alternatives like model trees are not truly comprehensible. • We suggest a new representation language for the extracted models; i.e., standard regression trees, but augmented with valid and sharp prediction intervals in the leaves. • In the extensive empirical investigation, the validity of the extracted models is demonstrated. • In addition, it is shown how normalization can be used to provide individualized prediction intervals, thus providing highly informative extracted models. Tools for understanding and explaining complex predictive models are critical for user acceptance and trust. One such tool is rule extraction, i.e., approximating opaque models with less powerful but interpretable models. Pedagogical (or black-box) rule extraction, where the interpretable model is induced using the original training instances, but with the predictions from the opaque model as targets, has many advantages compared to the decompositional (white-box) approach. Most importantly, pedagogical methods are agnostic to the kind of opaque model used, and any learning algorithm producing interpretable models can be employed for the learning step. The pedagogical approach has, however, one main problem, clearly limiting its utility. Specifically, while the extracted models are trained to mimic the opaque, there are absolutely no guarantees that this will transfer to novel data. This potentially low test set fidelity must be considered a severe drawback, in particular when the extracted models are used for explanation and analysis. In this paper, a novel approach, solving the problem with test set fidelity by utilizing the conformal prediction framework, is suggested for extracting interpretable regression models from opaque models. The extracted models are standard regression trees, but augmented with valid prediction intervals in the leaves. Depending on the exact setup, the use of conformal prediction guarantees that either the test set fidelity or the test set accuracy will be equal to a preset confidence level, in the long run. In the extensive empirical investigation, using 20 publicly available data sets, the validity of the extracted models is demonstrated. In addition, it is shown how normalization can be used to provide individualized prediction intervals, thus providing highly informative extracted models. [ABSTRACT FROM AUTHOR]

• We developed two distinct strategies and two algorithms that address certain challenges associated with employing adversarial perturbation algorithms in the context of MIL. Furthermore, the efficacy of the algorithm has been verified across diverse input data scenarios. • The resulting adversarial bags and the proposed methods can be deemed valuable knowledge or prior experience for future learners. This is because MI-CAP can generate perturbations that remain unaffected by other bags, and the perturbations generated by MI-UAP can be conveniently stored as part of the knowledge base. • We have demonstrated the generalizability of these perturbations through experiments, illustrating that perturbations generated by one neural network can fool other networks as well. Furthermore, we have put forth a straightforward strategy to mitigate the impact of these perturbations as much as possible. Multi-instance learning (MIL) is a recent machine learning paradigm which is immensely useful in various real-life applications, like image analysis, video anomaly detection, text classification, etc. It is well known that most of the existing machine learning classifiers are highly vulnerable to adversarial perturbations. Since MIL is a weakly supervised learning, where information is available for a set of instances, called bag and not for every instance, adversarial perturbations can be fatal. In this paper, we have proposed two adversarial perturbation methods to analyze the effect of adversarial perturbations to interpret the vulnerabilities of MIL methods. Out of the two algorithms, one can be customized for every bag, and the other is a universal one, which can affect all bags in a given data set and thus has some generalizability. Furthermore, through simulations, we have demonstrated the efficacy of the proposed algorithms in fooling state-of-the-art MIL approaches, such that these models make incorrect predictions regarding the label assigned to the bag. Finally, we have discussed, through experiments, about taking care of these kind of adversarial perturbations through a simple strategy. Source codes are available at https://github.com/InkiInki/MI-UAP. [ABSTRACT FROM AUTHOR]

• Using ℓ 20 norm constraint to obtain a pure sparse subspace. • Providing an algorithm to obtain a closed-form solution for ℓ 20 norm constraint problem. • The high efficiency of algorithm is guaranteed by the convergence proof. • Exploiting local discriminative structure to learn a better subspace. Clustering is a fashion method applied in machine learning tasks. However, high dimensional data brings many obstacles for clustering approaches. To address such a problem, the unsupervised feature selection (UFS) method can be incorporated into clustering to reduce dimensionality. In general, most of the UFS methods adopt ℓ 2 , 1 -norm for subspace sparsity learning. However, its sparsity highly relies on the setting of trade-off parameter, which may lead to instability of ranking results and the difficulty in obtaining the optimal solution of projection matrix. In this paper, we propose to directly learn an absolutely row-sparsity subspace via the ℓ 2 , 0 -norm constraint, called Sparse constraint and Local learning for Unsupervised Feature Selection (SLUFS). It is an ideal sparse subspace constraint which can overcome the drawbacks of the ℓ 2 , 1 -norm. However, optimizing the ℓ 2 , 0 -norm constraint is an NP-hard problem, and at present, only some approximate solutions can be given, but the convergence can not be guaranteed. To tackle this challenge, we design a novel alternative iterative algorithm to directly optimize the ℓ 2 , 0 -norm based model. Most importantly, our strategy can obtain a closed-form solution with strict convergence guarantee. Comprehensive experiments are conducted on several real-world datasets to evaluate the performance of SLUFS with comparison to several related state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• We propose a tree representation based on constrained matrices that leads to two new generic linear programming formulations of the optimal decision tree problem allowing to easily integrate domain-knowledge constraints. • Both formulations give the possibility for users to easily control complexity through the constraints on the number of leaf nodes and the maximum depth of the decision tree. • We theoretically show that elements of these matrices give the ability to easily formalise domain knowledge as constraints to improve trustworthiness. When applied to critical domains, machine learning models usually need to comply with prior knowledge and domain-specific requirements. For example, one may require that a learned decision tree model should be of limited size and fair, so as to be easily interpretable, trusted, and adopted. However, most state-of-the-art models, even on decision trees, only aim to maximising expected accuracy. In this paper, we propose a framework in which a diverse family of prior and domain knowledge can be formalised and imposed as constraints on decision trees. This framework is built upon a newly introduced tree representation that leads to two generic linear programming formulations of the optimal decision tree problem. The first one targets binary features, while the second one handles continuous features without the need for discretisation. We theoretically show how a diverse family of constraints can be formalised in our framework. We validate the framework with constraints on several applications and perform extensive experiments, demonstrating empirical evidence of comparable performance w.r.t. state-of-the-art tree learners. [ABSTRACT FROM AUTHOR]

• Gaussian-product-Gamma prior with sparsity analysis for TT representation. • Variational Inference algorithm with automatic rank selection. • Complexity analysis and efficiency improvement for the algorithm. • Extensive experimental results on synthetic data and real-world datasets. As a powerful tool in analyzing multi-dimensional data, tensor train (TT) decomposition shows superior performance compared to other tensor decomposition formats. Existing TT decomposition methods, however, either easily overfit with noise, or require substantial fine-tuning to strike a balance between recovery accuracy and model complexity. To avoid the above shortcomings, this paper treats the TT decomposition in a fully Bayesian perspective, which includes automatic TT rank determination and noise power estimation. Theoretical justification on adopting the Gaussian-product-Gamma priors for inducing sparsity on the slices of the TT cores is provided, thus allowing the model complexity to be automatically determined even when the observed tensor data is noisy and contains many missing values. Furthermore, using the variational inference framework, an effective learning algorithm on the probabilistic model parameters is derived. Simulations on synthetic data demonstrate that the proposed algorithm accurately recovers the underlying TT structure from incomplete noisy observations. Further experiments on image and video data also show its superior performance to other existing TT decomposition algorithms. [ABSTRACT FROM AUTHOR]

• Review of the most commonly used distance measures in machine learning • Analysis of their main properties, applications and key aspects to consider • The similarity analysis shows a high degree of correlation between all the measures • Evaluation of classification and clustering performance, noise tolerance and runtime • Canberra distance shows the best overall performance and the highest tolerance to noise Many machine learning and data mining tasks are based on distance measures, so a large amount of literature addresses this aspect somehow. Due to the broad scope of the topic, this paper aims to provide an overview of the use of these measures in the most common machine learning problems, pointing out those aspects to consider to choose the most appropriate measure for a particular task. For this purpose, the most recent works addressing the subject were reviewed and seven of the most commonly used measures were analyzed, investigating in detail their main properties and applications. Different experiments were carried out to study their relationships and compare their performance. The degradation of the results in the presence of noise was also considered, as well as the execution time required by each measure. [ABSTRACT FROM AUTHOR]

· A novel method (RPI-CapsuleGAN) to predict RNA-protein interactions (RPI). · Use the elastic net feature selection method to screen high-correlation features as model input features to reduce the complexity of the model operation and avoid overfitting. · The convolutional attention mechanism is introduced into a generative adversarial capsule network to construct a classifier model for the first time. · Predict and analyze RPI network, and analyze the interpretability of RNA-protein interaction network. · RPI-CapsuleGAN increases the prediction performance on independent testing datasets compared with other state-of-the-art methods. RNA-protein interactions (RPI) play a crucial regulatory role in cellular physiological processes. The study and prediction of RPIs can be insightful for exploring disease mechanisms and drug target design. Traditional RPI prediction methods relied mainly on tedious and expensive biological experiments, and there is an increasing interest in developing more cost-effective computational methods to predict RPIs. This work proposes an interpretable RPI-CapsuleGAN method for RPI prediction based on a generative adversarial capsule network with a convolutional block attention module. First, RPI-CapsuleGAN extracts and fuses multiple features to characterize RNA and protein sequences. Subsequently, the elastic net feature selection method is used to retain features that are highly informative to RPI prediction. Finally, we introduce a convolutional attention mechanism into the generative adversarial capsule network for the first time in order to construct the RPI prediction framework, which is shown to improve the model feature learning of interpretable and expression ability, and effectively solves the problem of the disappearance of the model spatial structure hierarchy. Based on a five-fold cross-validation test, the prediction accuracy of the RPI-CapsuleGAN method reaches 97.1%, 88.8%, 92.5%, 97.3%, and 87.8% for datasets RPI488, RPI369, RPI2241, RPI1807, and RPI1446. The RPI-CapsuleGAN method has higher accuracy than state-of-the-art RPI prediction methods that use the same datasets. In the test dataset NPInter227 constructed in this paper, five groups of test sets are composed of positive samples and five groups of negative samples, the prediction accuracy reaches 97.38%, 96.48%, 97.38%, 97.81%, and 97.15%, respectively, outperforming other mainstream deep learning algorithms. In addition, RPI-CapsuleGAN obtained better results for the prediction of independent test datasets. Extensive experiments detailed here show that RPI-CapsuleGAN can provide an efficient, accurate, and stable method for RPI prediction. [ABSTRACT FROM AUTHOR]

• We present a new research problem that is inspired by common scenarios encountered in the analysis of financial time-series. • A solution to the research problem is presented utilizing the TABL network, and its effectiveness is validated through experiments conducted on three commonly encountered scenarios. • By presenting a CNN-based solution, we demonstrate that our proposed method is applicable to other types of neural networks. • We conducted extensive experiments using two datasets from distinct markets with varying characteristics, while considering various real-world scenarios. Deep Learning models have become dominant in tackling financial time-series analysis problems, overturning conventional machine learning and statistical methods. Most often, a model trained for one market or security cannot be directly applied to another market or security due to differences inherent in the market conditions. In addition, as the market evolves over time, it is necessary to update the existing models or train new ones when new data is made available. This scenario, which is inherent in most financial forecasting applications, naturally raises the following research question: How to efficiently adapt a pre-trained model to a new set of data while retaining performance on the old data, especially when the old data is not accessible? In this paper, we propose a method to efficiently retain the knowledge available in a neural network pre-trained on a set of securities and adapt it to achieve high performance in new ones. In our method, the prior knowledge encoded in a pre-trained neural network is maintained by keeping existing connections fixed, and this knowledge is adjusted for the new securities by a set of augmented connections, which are optimized using the new data. The auxiliary connections are constrained to be of low rank. This not only allows us to rapidly optimize for the new task but also reduces the storage and run-time complexity during the deployment phase. The efficiency of our approach is empirically validated in the stock mid-price movement prediction problem using a large-scale limit order book dataset. Experimental results show that our approach enhances prediction performance as well as reduces the overall number of network parameters. [ABSTRACT FROM AUTHOR]

• We innovatively introduce anomaly detection technique to trajectory prediction area, which presents a novel method to guide the reasonable use of stochastic trajectory prediction models. • We flexibly use and reasonably combine the existing design experience in the fields of anomaly detection and trajectory prediction, to construct trajectory anomaly detection models to examine the rationality of stochastic trajectory prediction results. • We use the automated machine learning technique, to automatically search the optimal trajectory anomaly detection models in a well-designed search space by reinforcement learning. • Our method can lead to a higher performance of stochastic trajectory prediction models in real applications. Trajectory Prediction (TP) is an important research topic in computer vision and robotics fields. Recently, many stochastic TP models have been proposed to deal with this problem and have achieved better performance than the traditional models with deterministic trajectory outputs. However, these stochastic models can generate a number of future trajectories with different qualities. They are lack of self-evaluation ability, that is, to examine the rationality of their prediction results, thus failing to guide users to identify high-quality ones from their candidate results. This hinders them from playing their best in real applications. In this paper, we make up for this defect and propose TPAD, a novel TP evaluation method based on the trajectory Anomaly Detection (AD) technique. In TPAD, we firstly combine the Automated Machine Learning (AutoML) technique and the experience in the AD and TP field to automatically design an effective trajectory AD model. Then, we utilize the learned trajectory AD model to examine the rationality of the predicted trajectories, and screen out good TP results for users. Extensive experimental results demonstrate that TPAD can effectively identify near-optimal prediction results, improving stochastic TP models' practical application effect. [ABSTRACT FROM AUTHOR]

• Discretization is commonly used in naive Bayes. • Few approaches consider the effect of multiple occurrences of a single value. • We propose Rigorous Non-Disjoint Discretization (RNDD) in this paper. • Extensive experimental results validate the effectiveness of RNDD. Naive Bayes is a classical machine learning algorithm for which discretization is commonly used to transform quantitative attributes into qualitative attributes. Of numerous discretization methods, Non-Disjoint Discretization (NDD) proposes a novel perspective by forming overlapping intervals and always locating a value toward the middle of an interval. However, existing approaches to NDD fail to adequately consider the effect of multiple occurrences of a single value — a commonly occurring circumstance in practice. By necessity, all occurrences of a single value fall within the same interval. As a result, it is often not possible to discretize an attribute into intervals containing equal numbers of training instances. Current methods address this issue in an ad hoc manner, reducing the specificity of the resulting atomic intervals. In this study, we propose a non-disjoint discretization method for NB, called Rigorous Non-Disjoint Discretization (RNDD), that handles multiple occurrences of a single value in a systematic manner. Our extensive experimental results suggest that RNDD significantly outperforms NDD along with all other existing state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

• Toxonomy - an extensive overview of classical machine learning and contemporary deep learning within X-ray security imaging. • Datasets - an overview of the large datasets used to train deep learning approaches within the field. • Open problems - discussion of the open problems, current challenges, and future directions based on the current trends within computer vision. X-ray security screening is widely used to maintain aviation/transport security, and its significance poses a particular interest in automated screening systems. This paper aims to review computerised X-ray security imaging algorithms by taxonomising the field into conventional machine learning and contemporary deep learning applications. The first part briefly discusses the classical machine learning approaches utilised within X-ray security imaging, while the latter part thoroughly investigates the use of modern deep learning algorithms. The proposed taxonomy sub-categorises the use of deep learning approaches into supervised and unsupervised learning, with a particular focus on object classification, detection, segmentation and anomaly detection tasks. The paper further explores well-established X-ray datasets and provides a performance benchmark. Based on the current and future trends in deep learning, the paper finally presents a discussion and future directions for X-ray security imagery. [ABSTRACT FROM AUTHOR]

• Convolutional Neural Networks can be improved in terms of the classification performance and robustness by using variable weight structures. • Analysis of different data processing methods, models' robustness and statistical properties. • Comparative analysis of variable weight convolutional neural networks and other widely used machine learning techniques. • Medical applications to the classification of seizure phases and types. Deep learning techniques have recently achieved impressive results and raised expectations in the domains of medical diagnosis and physiological signal processing. The widely adopted methods include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, the existing models possess static connection weights between layers, which might limit the generalization capability and the classification performance of the models as the weights of different layers are fixed after training. Furthermore, to deal with a large amount of data, a neural network with a sufficiently large size is required. This paper proposes the variable weight convolutional neural networks (VWCNNs), which are a type of network structure employing dynamic weights instead of static weights in their convolutional layers and fully-connected layers. VWCNNs are able to adapt to different characteristics of input data and can be viewed as an infinite number of traditional, fixed-weight CNNs. We will show that the proposed VWCNN structure outperforms the conventional CNN in terms of the classification accuracy, generalization capability, and robustness when the inputs are contaminated by noise. In this paper, VWCNNs are applied to the classification of three seizure phases (seizure-free, pre-seizure and seizure) based on measured electroencephalography (EEG) data. VWCNNs achieve 100% test accuracy and show strong robustness in the classification of the three seizure phases, and thus show the potential to be a useful classification tool for medical diagnosis. Furthermore, the classification of seven types of seizures is investigated in this paper using the world's largest open source database of seizure recordings, TUH EEG seizure corpus. Comparisons with conventional CNNs, RNN, MobileNet, ResNet, DenseNet and traditional machine learning methods including random forest, decision tree, support vector machine, K-nearest neighbours, standard neural networks, and Naïve Bayes are being conducted using realistic test data sets. The results demonstrate that VWCNNs have advantages over other classifiers in terms of classification accuracy and robustness. [ABSTRACT FROM AUTHOR]

• The first survey paper for pedestrian attributes recognition (PAR). • Give a brief introduction to existing pedestrian attributes recognition algorithms. • Give various research directions for PAR. Pedestrian Attribute Recognition (PAR) is an important task in computer vision community and plays an important role in practical video surveillance. The goal of this paper is to review existing works using traditional methods or based on deep learning networks. Firstly, we introduce the background of pedestrian attribute recognition, including the fundamental concepts and formulation of pedestrian attributes and corresponding challenges. Secondly, we analyze popular solutions for this task from eight perspectives. Thirdly, we discuss the specific attribute recognition, then, give a comparison between deep learning and traditional algorithm based PAR methods. After that, we show the connections between PAR and other computer vision tasks. Fourthly, we introduce the benchmark datasets, evaluation metrics in this community, and give a brief performance comparison. Finally, we summarize this paper and give several possible research directions for PAR. The project page of this paper can be found at: https://sites.google.com/view/ahu-pedestrianattributes/. [ABSTRACT FROM AUTHOR]

The paper is devoted to two problems: (1) reinforcement of SVM algorithms, and (2) justification of memorization mechanisms for generalization. (1) Current SVM algorithm was designed for the case when the risk for the set of nonnegative slack variables is defined by l 1 norm. In this paper, along with that classical l 1 norm, we consider risks defined by l 2 norm and l ∞ norm. Using these norms, we formulate several modifications of the existing SVM algorithm and show that the resulting modified SVM algorithms can improve (sometimes significantly) the classification performance. (2) Generalization ability of existing learning algorithms is usually explained by arguments involving uniform convergence of empirical losses to the corresponding expected losses over a given set of functions. However, along with bounds for uniform convergence of empirical losses to the expected losses, the VC theory also provides bounds for relative uniform convergence. These bounds lead to a more accurate estimate of the expected loss. Advanced methods of estimating of expected risk of error have to leverage these bounds, which also support mechanisms of training data memorization, which, as the paper demonstrates, can improve classification performance. [ABSTRACT FROM AUTHOR]

• Three types of imbalanced problems are common challenges in multi-label classification: imbalance within labels, between labels, and among label-sets. • A comprehensive and up-to-date review of methods for addressing imbalanced problems in multi-label classification is presented. • Methods for assessing imbalance level and performance measures in the multi-label scenario are surveyed. • Comparative analysis of the reviewed methods and their limitations are discussed to guide future directions. Multi-Label Classification (MLC) is an extension of the standard single-label classification where each data instance is associated with several labels simultaneously. MLC has gained much importance in recent years due to its wide range of application domains. However, the class imbalance problem has become an inherent characteristic of many multi-label datasets, where the samples and their corresponding labels are non-uniformly distributed over the data space. The imbalanced problem in MLC imposes challenges to multi-label data analytics which can be viewed from three perspectives: imbalance within labels, among labels, and label-sets. In this paper, we provide a review of the approaches for handling the imbalance problem in multi-label data by collecting the existing research work. As the first systematic study of approaches addressing an imbalanced problem in MLC, this paper provides a comprehensive survey of the state-of-the-art methods for imbalanced MLC, including the characteristics of imbalanced multi-label datasets, evaluation measures and comparative analysis of the proposed methods. The study also discusses important results reported so far in the literature and highlights some of their strengths and limitations to guide future research. [ABSTRACT FROM AUTHOR]

• We re-investigate the pipeline of fine-grained visual categorization (FGVC) techniques from the view of human visual recognition system, and propose a novel Attention-Shift based Deep Neural Network (AS-DNN) for automatic parts locating and semantic correlation learning. • We propose an end-to-end trainable sub-network structure C sft to simulate the attention-shift process. C sft locates the discriminative regions automatically and encodes and decodes the semantic relations among diverse discriminative parts iteratively. • Comprehensive experiments show that AS-DNN achieves state-of-the-art performances in three widely used challenging datasets. Moreover, the visualization of located discriminative parts proves the robustness of AS-DNN in complex backgrounds and postures. Fine-grained visual categorization (FGVC) has attracted extensive attention in recent years. The general pipeline of current FGVC techniques is to 1) locate the discriminative regions; 2) extract features from each region independently; and 3) feed the integrated features to a classifier. In this paper, we re-investigate the pipeline from the view of human visual recognition mechanisms. The perceiving of discriminative regions is a temporal processing by the human visual system (HVS) via the attention-shift mechanism. However, the existing independent feature extracting and one-pass feeding strategy ignore the inherent semantic relationships among discriminative regions, and thus is improper to model the attention-shift process properly. Therefore, in this paper, we propose a novel end-to-end FGVC network structure named Attention-Shift based Deep Neural Network (AS-DNN) to locate the discriminative regions automatically and encode the semantic correlations iteratively. AS-DNN consists of two channels: 1) the global perception channel C glb and 2) the attention-shift channel C sft , simulating the global perception and the attention-shift mechanism, respectively. Experimental results show that AS-DNN achieves state-of-the-art performances by outperforming both the CNN-based weakly or strongly-supervised FGVC algorithms on several widely-used fine-grained datasets, and the visualization of attention regions exhibit that the proposed method can locate the discriminative regions robustly in complex backgrounds and postures. [ABSTRACT FROM AUTHOR]

• A new feature selection approach is proposed, which combines and utilizes multiple individual methods in order to achieve a more generalized solution. • A hybrid solution is proposed in the paper which combines the given, available (supervised, state-of-the-art) feature selection techniques that have their own specific, but fixed feature evaluation measures/metrics. • Adaptivity of the proposed algorithm is realized in such a way that at an individual step of the feature selection algorithm it iterates not only in the space of the variables but in the space of available features selection techniques, too. • Different directions of tests were applied: linear and non-linear dependencies with varying data distribution, noise and outliers; using benchmark datasets of the UCI Machine Learning Repository and also own real-life datasets; comparison to recent state-of-the-art feature selection methods. • T The proposed AHFS nearly doubles the accuracy (resulting in around half value for the related modeling error) compared to the individual methods, making it a superior feature selection algorithm . This paper deals with the problem of integrating the most suitable feature selection methods for a given problem in order to achieve the best feature order. A new, adaptive and hybrid feature selection approach is proposed, which combines and utilizes multiple individual methods in order to achieve a more generalized solution. Various state-of-the-art feature selection methods are presented in detail with examples of their applications and an exhaustive evaluation is conducted to measure and compare the their performance with the proposed approach. Results prove that while the individual feature selection methods may perform with high variety on the test cases, the combined algorithm steadily provides noticeably better solution. [ABSTRACT FROM AUTHOR]

• Providing the condition under which an AHC does not effectively extract clusters. • Introducing the entanglement to measure the dendrogram quality. • Identifying the root cause of a density bias of traditional AHC algorithms. • Improving AHC performance with a data-dependent kernel. Agglomerative hierarchical clustering (AHC) is one of the popular clustering approaches. AHC generates a dendrogram that provides richer information and insights from a dataset than partitioning clustering. However, a major problem with existing distance-based AHC methods is: it fails to effectively identify adjacent clusters with varied densities, regardless of the cluster extraction methods applied to the resultant dendrogram. This paper aims to reveal the root cause of this issue and provides a solution by using a data-dependent kernel. We analyse the condition under which existing AHC methods fail to effectively extract clusters, and give the reason why the data-dependent kernel is an effective remedy. This leads to a new approach to kernerlise existing hierarchical clustering algorithms including the traditional AHC algorithms, HDBSCAN, GDL, PHA and HC-OT. Our extensive empirical evaluation shows that the recently introduced Isolation Kernel produces a higher quality or purer dendrogram than distance, Gaussian Kernel and adaptive Gaussian Kernel in all the above mentioned AHC algorithms. [ABSTRACT FROM AUTHOR]

• We propose an interpretable partial differential network for SPLT-3D, where both horizontal and vertical interactions are modeled effectively. • We develop the theory of formulating 3D spatiotemporal dynamics with PDEs and design an effective strategy to apply it. • Experimental results show that our method can not only achieve better prediction performance, but also learn the correct PDEs. Neural network-based methods have been widely applied to spatiotemporal prediction tasks, such as video prediction and weather forecasting. However, most existing works are designed for prediction in 2D space, and 3D prediction has not been extensively studied. In this paper, we propose to leverage 3D partial differential equations (PDEs) for spatiotemporal prediction in 3D space, and further develop a novel 3D neural partial differential network. This is inspired by that 3D PDEs can model both horizontal and vertical information interactions by various partial derivatives. Moreover, they can also formulate physical knowledge by equations. To integrate 3D PDEs in neural networks, we first develop the theory of approximating 3D partial derivatives by 3D convolutions, and further present an effective strategy to utilize the theory in practice. Then based on the theory and strategy, we propose a novel 3D Neural Partial Differential Network for prediction, named NPDN-3D. Specifically, NPDN-3D consists of two pivotal modules: (1) a neural partial differential module for capturing low-order spatiotemporal dynamics. This module is the key for prediction, where the dynamics are formulated by commonly-used low-order 3D PDEs. (2) A residual module for capturing the remaining non-low-order dynamics. This module performs as an extensible plug-in to enhance the expressiveness of our model. Extensive experiments on two simulated datasets and two real datasets show that our method not only achieves better prediction but also learns the correct PDEs. [ABSTRACT FROM AUTHOR]

• Face expression analysis suffers from insufficiently annotated data. • We introduce the timid semi-supervised strategy based on diversity for face expression analysis. • The solution produces superior results in both expression recognition and action unit estimation. • Simple solutions based on diversity perform better when biased unlabelled data is available. [Display omitted] In the last years, semi–supervised learning has been proposed as a strategy with high potential for improving machine learning capabilities. Face expression recognition may highly benefit from such a technique, as accurate labeling is both difficult and costly, whereas millions of unlabeled images with human faces are available on the Internet, but without annotations. In this paper we evaluate the benefits of semi–supervised learning in the practical scenarios of face expression analysis. Our conclusion is that better performance is indeed achievable, but by methods that put a distinct emphasis on the diversity of exploring patterns in the unlabeled data domain. The evaluation is carried on multiple tasks such as detecting Action Units on EmotioNet, assessing Action Units intensity on the spontaneous DISFA database and, respectively, recognizing expressions on static images acquired in the wild, from the RAF-DB and FER+ databases. We show that, in these scenarios, a so–called timid semi–supervised learner is more robust and achieves higher performance than standard, confident semi–supervised learners. [ABSTRACT FROM AUTHOR]

• Form a weighted 0-norm non-negative matrix tri-factorization with global and local discriminant regularizers to simultaneously considers feature weights, data noise, local manifolds, and global scatter in co-clustering. • Optimize the formed objective function to solve the proposed co-clustering problem by an alternate update rule, whose convergence is proved in theory. • Verify our algorithm's duality, robustness, effectiveness, and competitiveness through comprehensive experiments on synthetic, corrupted and real datasets. In the past few decades, the clustering problem has made considerable progress, and co-clustering algorithms have attracted more attention. Compared with one-side clustering, co-clustering not only groups samples according to the distribution of features but also groups features according to the distribution of samples at the same time. This duality helps to explore the structural information of data, such as genes and texts. In this paper, a new co-clustering algorithm is proposed to simultaneously consider feature weights, data noise, local manifolds, and global scatter, named robust weighted co-clustering with global and local discrimination. Furthermore, an alternate update rule is put forward to optimize objective, theoretically proven to converge. Then, the algorithm's duality, robustness, and effectiveness have been verified on synthetic, corrupted, and real datasets, respectively. The runtime and parameter sensitivity of the algorithm are also analyzed. Finally, sufficient experiments clarify the competitiveness of our algorithm compared to other ones. [ABSTRACT FROM AUTHOR]

• We formalize a new learning framework named Multi-Dimensional Multi-Label (MDML) classification to learn from objects whose semantics need to be characterized with multiple heterogeneous label spaces as well as multi-label annotations in each label space. • We specifically design an approach named CLIM to solve the MDML problem which can consider label correlations within individual dimension and across multiple dimensions. • We evaluate the performance of CLIM over four real-world MDML data sets and the experimental results clearly validate the effectiveness of the proposed approach. In traditional classification framework, the semantics of each object is usually characterized by annotating a single class label from one homogeneous label space. Nonetheless, objects with rich semantics naturally arise in real-world applications whose properties need to be characterized in a more sophisticated manner. In this paper, a new classification framework named Multi-Dimensional Multi-Label (MDML) classification is investigated which models objects with rich semantics by encompassing heterogeneous label spaces and multi-label annotations. Specifically, MDML generalizes the traditional classification framework by assuming a number of heterogeneous label spaces to characterize semantics from different dimensions, where each object is further annotated with multiple class labels from each heterogeneous label space. To learn from MDML training examples, a first attempt named CLIM is proposed based on an augmented stacking strategy. Firstly, CLIM induces a base multi-label predictive model w.r.t. each label space by maximizing the likelihood of the observed multiple class labels. Secondly, the thresholding predictions from all base models are used to augment the original feature space to yield stacked multi-label predictive models. The two-level models are refined alternately via empirical threshold tuning. Experiments on four real-world MDML data sets validate the effectiveness of CLIM in learning from training examples with heterogeneous label spaces and multi-label annotations. [ABSTRACT FROM AUTHOR]

• Both communication efficiency and robustness for convex distributed learning are taken into accounts simultaneously, which is very rare in existing related literatures. • The current work develops two communication-efficient and robust distributed learning algorithms for convex problems. Particularly, the proposed algorithms are provably robust against Byzantine failures, and also achieve optimal statistical rates for strong convex losses and convex (non-smooth) penalties. • For typical statistical models such as generalized linear models, our results show that statistical errors dominate optimization errors in finite iterations. • Simulated and real data experiments are conducted to demonstrate the comparable performance of our algorithms. Communication efficiency and robustness are two major issues in modern distributed learning frameworks. This is due to the practical situations where some computing nodes may have limited communication power or may behave adversarial behaviors. To address the two issues simultaneously, this paper develops two communication-efficient and robust distributed learning algorithms for convex problems. Our motivation is based on surrogate likelihood framework and the median and trimmed mean operations. Particularly, the proposed algorithms are provably robust against Byzantine failures, and also achieve optimal statistical rates for strong convex losses and convex (non-smooth) penalties. For typical statistical models such as generalized linear models, our results show that statistical errors dominate optimization errors in finite iterations. Simulated and real data experiments are conducted to demonstrate the numerical performance of our algorithms. [ABSTRACT FROM AUTHOR]