Search

Time-stamped data are ubiquitous in our daily life, such as twitter data, academic papers and sensor data. Finding clusters and their evolutionary trends in time-stamped data sets are receiving increasing attention from researchers. Most existing methods, however, can only tackle the clustering problem of a data set without time-stamped information which is inherent in almost all the data objects. Actually, not only the performance can be improved by effectively incorporating the time-stamped information in the clustering process on most data sets, but also we can find the evolutionary trends of the clusters with time information. In this paper, we introduce an approach for clustering time-stamped data and discovering the evolutionary trends of the clusters by using Multiple Nonnegative Matrices Factorization (MNMF) with smooth constraint over time. To utilize time-stamped information in the clustering process, an extra object-time matrix is constructed in our proposed method. Then, we jointly factorize multiple feature matrices using smooth constraint to perform the object-time matrix to obtain the clusters and their evolutionary trends. Experimental results on real data sets demonstrate that our proposed approach outperforms the comparative algorithms with respect to Fscore, NMI or Entropy. [ABSTRACT FROM AUTHOR]

Abstract: For high dimensional data a large portion of features are often not informative of the class of the objects. Random forest algorithms tend to use a simple random sampling of features in building their decision trees and consequently select many subspaces that contain few, if any, informative features. In this paper we propose a stratified sampling method to select the feature subspaces for random forests with high dimensional data. The key idea is to stratify features into two groups. One group will contain strong informative features and the other weak informative features. Then, for feature subspace selection, we randomly select features from each group proportionally. The advantage of stratified sampling is that we can ensure that each subspace contains enough informative features for classification in high dimensional data. Testing on both synthetic data and various real data sets in gene classification, image categorization and face recognition data sets consistently demonstrates the effectiveness of this new method. The performance is shown to better that of state-of-the-art algorithms including SVM, the four variants of random forests (RF, ERT, enrich-RF, and oblique-RF), and nearest neighbor (NN) algorithms. [Copyright &y& Elsevier]

Abstract: This paper proposes a new method to weight subspaces in feature groups and individual features for clustering high-dimensional data. In this method, the features of high-dimensional data are divided into feature groups, based on their natural characteristics. Two types of weights are introduced to the clustering process to simultaneously identify the importance of feature groups and individual features in each cluster. A new optimization model is given to define the optimization process and a new clustering algorithm FG-k-means is proposed to optimize the optimization model. The new algorithm is an extension to k-means by adding two additional steps to automatically calculate the two types of subspace weights. A new data generation method is presented to generate high-dimensional data with clusters in subspaces of both feature groups and individual features. Experimental results on synthetic and real-life data have shown that the FG-k-means algorithm significantly outperformed four k-means type algorithms, i.e., k-means, W-k-means, LAC and EWKM in almost all experiments. The new algorithm is robust to noise and missing values which commonly exist in high-dimensional data. [Copyright &y& Elsevier]

Abstract: Clustering is one of the important data mining tasks. Nested clusters or clusters of multi-density are very prevalent in data sets. In this paper, we develop a hierarchical clustering approach—a cluster tree to determine such cluster structure and understand hidden information present in data sets of nested clusters or clusters of multi-density. We embed the agglomerative k-means algorithm in the generation of cluster tree to detect such clusters. Experimental results on both synthetic data sets and real data sets are presented to illustrate the effectiveness of the proposed method. Compared with some existing clustering algorithms (DBSCAN, X-means, BIRCH, CURE, NBC, OPTICS, Neural Gas, Tree-SOM, EnDBSAN and LDBSCAN), our proposed cluster tree approach performs better than these methods. [Copyright &y& Elsevier]

Wind power is a new type of green energy. Though it is economical to access and gather such energy, effectively matching the energy with consumers' demand is difficult, because of the fluctuate, intermittent and chaotic nature of wind speed. Hence, multi-step wind speed prediction becomes an important research topic. In this paper, we propose a novel deep learning method, DyanmicNet, for the problem. DynamicNet follows an encoder–decoder framework. To capture the fluctuate, intermittent and chaotic nature of wind speed, it leverages a time-variant structure to build the decoder, which is different from conventional encoder–decoder methods. In addition, a new neural block (ST-GRU-ODE) is developed, which can model the wind speed in a continuous manner by using the neural ordinary differential equation (ODE). To enhance the prediction performance, a multi-step training procedure is also put forward. Comprehensive experiments have been conducted on two real-world datasets, where wind speed is recorded in the form of two orthogonal components namely U-Wind and V-Wind. Each component can be illustrated as wind speed images. Experimental results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Anomaly detection in multivariate time series is of critical importance in many real-world applications, such as system maintenance and Internet monitoring. In this article, we propose a novel unsupervised framework called SVD-AE to conduct anomaly detection in multivariate time series. The core idea is to fuse the strengths of both SVD and autoencoder to fully capture complex normal patterns in multivariate time series. An asymmetric autoencoder architecture is proposed, where two encoders are used to capture features in time and variable dimensions and a shared decoder is used to generate reconstructions based on latent representations from both dimensions. A new regularization based on singular value decomposition theory is designed to force each encoder to learn features in the corresponding axis with mathematical supports delivered. A specific loss component is further proposed to align Fourier coefficients of inputs and reconstructions. It can preserve details of original inputs, leading to enhanced feature learning capability of the model. Extensive experiments on three real world datasets demonstrate the proposed algorithm can achieve better performance on multivariate time series anomaly detection tasks under highly unbalanced scenarios compared with baseline algorithms. [ABSTRACT FROM AUTHOR]

As a pixel-wise dense forecast task, video prediction is challenging due to its high computation complexity, dramatic future uncertainty, and extremely complicated spatial–temporal patterns. Many deep learning methods are proposed for the task, which bring up significant improvements. However, they focus on modeling short-term spatial–temporal dynamics and fail to sufficiently exploit long-term ones. As a result, the methods tend to deliver unsatisfactory performance for a long-term forecast requirement. In this article, we propose a novel unified memory network (UNIMEMnet) for long-term video prediction, which can effectively exploit long-term motion-appearance dynamics and unify the short-term spatial–temporal dynamics and long-term ones in an architecture. In the UNIMEMnet, a dual branch multi-scale memory module is carefully designed to extract and preserve long-term spatial–temporal patterns. In addition, a short-term spatial–temporal dynamics module and an alignment and fusion module are devised to capture and coordinate short-term motion-appearance dynamics with long-term ones from our designed memory module. Extensive experiments on five video prediction datasets from both synthetic and real-world scenarios are conducted, which validate the effectiveness and superiority of our proposed method UNIMEMnet over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In wind speed prediction technologies, deep learning-based methods have achieved promising advantages. However, most existing methods focus on learning implicit knowledge in a data-driven manner but neglect some explicit knowledge from the physical theory of meteorological dynamics, failing to make stable and long-term predictions. In this paper, we explore introducing explicit physical knowledge into neural networks and propose Physical Equations Predictive Network (PEPNet) for multi-step wind speed predictions. In PEPNet, a new neural block called the Augmented Neural Barotropic Equations (ANBE) block is designed as its key component, which aims to capture the wind dynamics by combining barotropic primitive equations and deep neural networks. Specifically, the ANBE block adopts a two-branch structure to model wind dynamics, where one branch is physic-based and the other is data-driven-based. The physic-based branch constructs temporal partial derivatives of meteorological elements (including u-component wind, v-component wind, and geopotential height) in a new Neural Barotropic Equations Unit (NBEU). The NBEU is developed based on the barotropic primitive equations mode in numerical weather prediction (NWP). Besides, considering that the barotropic primitive mode is a crude assumption of atmospheric motion, another data-driven-based branch is developed in the ANBE block, which aims at capturing meteorological dynamics beyond barotropic primitive equations. Finally, the PEPNet follows a time-variant structure to enhance the model's capability to capture wind dynamics over time. To evaluate the predictive performance of PEPNet, we have conducted several experiments on two real-world datasets. Experimental results show that the proposed method outperforms the state-of-the-art techniques and achieve optimal performance. [ABSTRACT FROM AUTHOR]

Abstract Most of existing image categorizations assume that the given datasets have a good resolution and quality. However, the assumption is often violated in real applications. In this paper, we study the low-resolution (LR) image categorization. By utilizing labeled high-resolution (HR) images as auxiliary information, we formulate the problem as a heterogeneous domain adaptation problem and propose a Discriminative Joint Distribution Adaptation (DJDA) model to solve it. The DJDA model projects both LR and HR images into an intermediate subspace with a well-designed objective function, where the distance between classes is expected to be enlarged and the distribution divergence to be reduced. As a result, the discriminative knowledge for HR images can be transferred effectively to LR images. Experimental results demonstrate the proposed DJDA method produces significantly superior categorization accuracies against state-of-the-art competitors. [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]

The real-world networking data may contain different types of attribute views and relational view. Hence, it is desirable to collectively use available attribute views and relational view in order to build effective learning models. We call this framework multi-attribute and relational learning. Collective classification is one of the popular approaches that can handle both attribute and relational information for network data. However, in collective classification only one type of attribute and relational view is involved and little attention is received for multi-attribute and relational learning. In this paper, we propose a new semi-supervised collective classification approach, called hypergraph regularized generative model (HRGM), for multi-attribute and relational learning. In the approach, a generative model based on the Probabilistic Latent Semantic Analysis (PLSA) method is developed to leverage attribute information, and a hypergraph regularizer is incorporated to effectively exploit higher-order relational information among the data samples. Experimental results on various data sets have demonstrated the effectiveness of the proposed HRGM, and revealed that our approach outperforms existing collective classification methods and multi-view classification methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

Regional wind speed prediction plays an important role in the development of wind power, which is usually recorded in the form of two orthogonal components, namely U-wind and V-wind. The regional wind speed has the characteristics of diverse variations, which are reflected in three aspects: (1) The spatially diverse variations of regional wind speed indicate that wind speed has different dynamic patterns at different positions; (2) The distinct variations between U-wind and V-wind denote that U-wind and V-wind at the same position exhibit different dynamic patterns; (3) The non-stationary variations of wind speed represent that the intermittent and chaotic nature of wind speed. In this paper, we propose a novel framework named Wind Dynamics Modeling Network (WDMNet) to model the diverse variations of regional wind speed and make accurate multi-step predictions. To jointly capture the spatially diverse variations and the distinct variations between U-wind and V-wind, WDMNet leverages a new neural block called Involution Gated Recurrent Unit Partial Differential Equation (Inv-GRU-PDE) as its key component. The block adopts involution to model spatially diverse variations and separately constructs hidden driven PDEs of U-wind and V-wind. The construction of PDEs in this block is achieved by a new Involution PDE (InvPDE) layers. Besides, a deep data-driven model is also introduced in Inv-GRU-PDE block as the complement to the constructed hidden PDEs for sufficiently modeling regional wind dynamics. Finally, to effectively capture the non-stationary variations of wind speed, WDMNet follows a time-variant structure for multi-step predictions. Comprehensive experiments have been conducted on two real-world datasets. Experimental results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Traffic flow prediction (TFP) has attracted increasing attention with the development of smart city. In the past few years, neural network-based methods have shown impressive performance for TFP. However, most of previous studies fail to explicitly and effectively model the relationship between inflows and outflows. Consequently, these methods are usually uninterpretable and inaccurate. In this paper, we propose an interpretable local flow attention (LFA) mechanism for TFP, which yields three advantages. (1) LFA is flow-aware. Different from existing works, which blend inflows and outflows in the channel dimension, we explicitly exploit the correlations between flows with a novel attention mechanism. (2) LFA is interpretable. It is formulated by the truisms of traffic flow, and the learned attention weights can well explain the flow correlations. (3) LFA is efficient. Instead of using global spatial attention as in previous studies, LFA leverages the local mode. The attention query is only performed on the local related regions. This not only reduces computational cost but also avoids false attention. Based on LFA, we further develop a novel spatiotemporal cell, named LFA-ConvLSTM (LFA-based convolutional long short-term memory), to capture the complex dynamics in traffic data. Specifically, LFA-ConvLSTM consists of three parts. (1) A ConvLSTM module is utilized to learn flow-specific features. (2) An LFA module accounts for modeling the correlations between flows. (3) A feature aggregation module fuses the above two to obtain a comprehensive feature. Extensive experiments on two real-world datasets show that our method achieves a better prediction performance. We improve the RMSE metric by 3.2%–4.6%, and the MAPE metric by 6.2%–6.7%. Our LFA-ConvLSTM is also almost 32% faster than global self-attention ConvLSTM in terms of prediction time. Furthermore, we also present some visual results to analyze the learned flow correlations. [ABSTRACT FROM AUTHOR]

• Unsupervised model for sequence anomaly detection. • Propose a novel spectral regularizer to restrict latent spaces of autoencoders. • Statistical analysis on DWT coefficients to mine unique features of normal sequences. • Design a Weight Controller to gain sample-adaptive regularization weights. Nowadays, systems or entities are usually monitored by devices, generating large amounts of time series. Detecting anomalies in them help prevent potential losses, thus arousing much research interest. Existing studies have adopted autoencoders to detect anomalies, where reconstruction errors are used to indicate outliers. However, sometimes autoencoders may also reconstruct anomalies well due to the learned general features in latent spaces. To solve the above problem, we propose to regularize autoencoders to grasp specific features of normal sequences. Specifically, spectral unique patterns are captured by statistical analysis on discrete wavelet transform (DWT) coefficients of input sequences, restricting latent spaces to reflect unique patterns of normal sequences in both time and frequency domains. Furthermore, a Weight Controller calculating sample-adaptive regularization weights is designed to fully utilize the regularization effect. Extensive experiments on three public benchmarks demonstrate the effectiveness and superiority of the proposed model compared with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting aims to predict the rainfall distribution within a short-term period. However, it pays the same attention to all locations instead of emphasizing those regions with heavy rainfall that has more threats to human activity. Therefore, we develop an important task named Heavy Rainfall Forecast (HRF), which mainly focuses on the movement and change of heavy rainfall areas. It sets aside one hour to give meteorological administration sufficient time to issue warning information. To tackle this task, firstly, we rebuild the meteorological radar dataset based on three criteria to obtain the samples involving heavy rainfall. Secondly, we propose the Location-Refining (LR) neural network to combine the advantages of the optical flow-based and deep learning-based methods in predicting higher intensity and more accurate position, respectively. LR neural network consists of a location network and a refining network. The former is responsible for the accurate predictions of position and trend of rainfall, and the later accounts for more accurately estimating the intensity. To make the model pay more attention to the high echo region, we design new loss functions and introduce auxiliary information of high echo values. A series of experiments show that our model has a significant improvement on this task. Specifically, compared with existing methods, we improve the valid mean square error by 6.4% for the threshold being 20 and 15.1% for the threshold being 30. The critical success indexes are improved by 12.8% for the threshold being 20 and 24.8% for the threshold being 30. We also improve the heidke skill score by 9.9% for the threshold being 20 and 21.4% for the threshold being 30. Furthermore, the proposed framework can be well transferred to other deep learning-based models, and improves their performance. • An important task for predicting heavy rainfall and criteria for building datasets. • A novel framework that effectively combines the advantages of two kinds of methods. • Focus more on heavy rainfall with a more reasonable optimization strategy. • Excellently transferred to other models and improve their performance. • Better practical performance for forecasting rainfall, especially for heavy rainfall. [ABSTRACT FROM AUTHOR]

The near-surface temperature prediction (NTP) is an important spatial–temporal forecast problem, which can be used to prevent temperature crises. Most of the previous approaches fail to explicitly model the long- and short-range spatial correlations simultaneously, which is critical to making an accurate temperature prediction. In this study, both long- and short-range spatial correlations are captured to fill this gap by a novel convolution operator named Long- and Short-range Convolution (LS-Conv). The proposed LS-Conv operator includes three key components, namely, Node-based Spatial Attention (NSA), Long-range Adaptive Graph Constructor (LAGC), and Long- and Short-range Integrator (LSI). To capture long-range spatial correlations, NSA and LAGC are proposed to evaluate node importance aiming at auto-constructing long-range spatial correlations, which is named as Long-range aware Graph Convolution Network (LR-GCN). After that, the Short-range aware Convolution Neural Network (SR-CNN) accounts for the short-range spatial correlations. Finally, LSI is proposed to capture both long- and short-range spatial correlations by intra-unifying LR-GCN and SR-CNN. Upon the proposed LS-Conv operator, a new model called Long- and Short-range for NPT (LS-NTP) is developed. Extensive experiments are conducted on two real-world datasets and the results demonstrate that the proposed method outperforms state-of-the-art techniques. The source code is available on GitHub: https://github.com/xuguangning1218/LS_NTP. • We jointly incorporate long- and short-range spatial correlations in NTP prediction. • Theoretical findings proved that the proposed LS-Conv is a general version of CNN. • We develop a new spatial–temporal model named LS-NTP for the NTP task. • Extensive experiments are conducted on two real-world datasets. [ABSTRACT FROM AUTHOR]

Regional wind speed prediction is an important spatiotemporal prediction problem which is crucial for optimizing wind power utilization. Nevertheless, the complex dynamics of wind speed pose a formidable challenge to prediction tasks. The evolving dynamics of wind could be governed by underlying physical principles that can be described by partial differential equations (PDE). This study proposes a novel approach called PDE-assisted network (PaNet) for regional wind speed prediction. In PaNet, a new architecture is devised, incorporating both PDE-based dynamics (PDE dynamics) and unknown dynamics. Specifically, this architecture establishes interactions between the two dynamics, regulated by an inter-dynamics communication unit that controls interactions through attention gates. Additionally, recognizing the significance of the initial state for PDE dynamics, an adaptive frequency-gated unit is introduced to generate a suitable initial state for the PDE dynamics by selecting essential frequency components. To evaluate the predictive performance of PaNet, this study conducts comprehensive experiments on two real-world wind speed datasets. The experimental results indicated that the proposed method is superior to other baseline methods. [ABSTRACT FROM AUTHOR]

According to their orbits, meteorological satellites can be divided into polar-orbiting satellites and geostationary satellites. Existing cloud detection methods mainly focus on polar-orbiting satellite datasets. The geostationary satellite datasets contain, in contrast, time-continuous frames of particular locations. The temporal consistent information in these consecutive frames aid to increase the detection accuracy, but is challenging to be exploited. Besides, powered by the advanced technology of satellites, the onboard cloud detection application becomes a trend. Considering that satellites have resource limitations on energy and storage, applications deployed on them should be lightweight enough. However, the existing cloud detection models never concentrated on this lightweight video cloud detection task before. In this task, the temporal consistent features provided by time-continuous frames should be exploited for accuracy enhancement with low resource consumption. To tackle this problem, we design a lightweight deep learning video cloud detection model: Adaptive Memory Attention Network (AMANet). The proposed network is based on the encoder–decoder structure. The encoder consists of two branches. In the main branch, spatial and semantic features of the current frame are extracted. In the TemporalAttentionFlow branch, the proposed PyramidEncodingModule adaptively extracts context information from frames in sequence based on their distance to the current frame. In addition, in the proposed AdaptiveMemoryAttentionModule, the temporal relation among frames is extracted and propagated adaptively. The lightweight decoder is designed to gradually recover the cloud masks to the same scale as the input image. Experiments on a Video Cloud Detection dataset based on the dataset Fengyun4aCloud demonstrate that the designed AMANet achieves a remarkable balance between accuracy and resource consumption in comparison with current cloud detection methods, lightweight semantic segmentation methods, and video semantic segmentation methods. • We design the AMANet to tackle the lightweight video cloud detection problem, which targets at cloud detection on consecutive frames captured by geostationary satellites. Currently, it is still a new task with few research. • AMANet uses an auto-encoder architecture with an AsymmSemBottleneck module for multi-scale feature extraction. It includes a TemporalAttentionFlow branch to enhance cloud detection accuracy by extracting relationships between consecutive frames. This relation is captured through a Pyramid Encoding Module (PEM) and an Adaptive Memory Attention Module (AMAM). A lightweight decoder effectively and efficiently recovers the feature map. • Extensive experiments on the Video Cloud Detection dataset based on the captured videos from Fengyun 4 (FY-4) satellite: Fengyun4aCloud demonstrate that AMANet can achieve the state-of-the-art balance between video cloud detection performance and efficiency. [ABSTRACT FROM AUTHOR]

Feature extraction is a prerequisite in many machine learning and data mining applications. As the advancement of data acquisition techniques, nowadays tensor objects are accumulated with respect to frequency or time information in a great number of fields. For instance color or hyperspectral faces in multichannel information, and human gait motion in time information are obtained. In this paper, we propose and develop a block principal component analysis (BPCA) to extract features for this kind of tensor objects. Our idea is to unfold tensor objects according to their spatial information and frequency/time information, and represent them in block matrix form. The corresponding covariance matrix for frequency/time information can be captured and used. The block eigen-decomposition of such covariance matrix is employed to seek for projection solution as features. Both reconstruction and classification problems can be solved via these projected features. Extensive experiments have been conducted on various face or gait databases to demonstrate the superiority of BPCA compared with existing methods such as PCA, (2D) 2 PCA, MPCA, and UMPCA in terms of effectiveness. Moreover, the proposed BPCA is competitively efficient compared to these existing methods. [ABSTRACT FROM AUTHOR]

k -Means algorithm has been proven an effective technique for clustering a large-scale data set. However, traditional k -means type clustering algorithms cannot effectively distinguish the discriminative capabilities of features in the clustering process. In this paper, we present a new k -means type clustering framework by extending W- k -means with an l 2 -norm regularization to the weights of features. Based on the framework, we propose the l 2 -Wkmeans algorithm by using conventional means as the centroids for clustering numerical data sets and present the l 2 -NOF and l 2 -NDM algorithms by using two different smooth modes representatives for clustering categorical data sets. At first, a new objective function is developed for the clustering framework. Then, the corresponding updating rules of the centroids, the membership matrix, and the weights of the features, are derived theoretically for the new algorithms. We conduct extensive experimental verifications to evaluate the performances of our proposed algorithms on numerical data sets and categorical data sets. Experimental studies demonstrate that our proposed algorithms delivers consistently promising results in comparison to the other comparative approaches, such basic k -means, W- k -means, MKM_NOF, MKM_NDM etc., with respects to four metrics: Accuracy, RandIndex, Fscore , and Normal Mutual Information ( NMI ). [ABSTRACT FROM AUTHOR]

The problem of effective stock trend prediction has aroused much attention these years for its profitability. The development of algorithmic trading drives explosive growth in fast and effective techniques for trend predictions. However, little attention has been paid to sample weighting schemes in this field. In this article, we propose a new sample weighting scheme for stock trend predictions based on financial features of companies. Specifically, stock trends are supposed to be determined by hidden market states represented by trend generating vectors. These vectors can be generated from financial features of companies. The scheme considers similarities between trend generating vectors (STGVs) when assigning weights to samples from different periods to differentiate their prediction capabilities. Similarity scores calculated with a proper metric are adopted to measure similarity. We train models with STGV to predict stock trends. Extensive experiments are conducted to figure out the most suitable similarity metric used in STGV and demonstrate its superiority over other sample weighting schemes together with its generalizability. • Propose the notion of trend generating vectors to represent hidden market states. • Assign weights to samples based on similarity between trend generating vectors. • Outperform other sample weighting schemes and enjoy high generalizability. [ABSTRACT FROM AUTHOR]

Topic models, such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), have shown impressive success in many fields. Recently, multi-view learning via probabilistic latent semantic analysis (MVPLSA), is also designed for multi-view topic modeling. These approaches are instances of generative model, whereas they all ignore the manifold structure of data distribution, which is generally useful for preserving the nonlinear information. In this paper, we propose a novel multiple graph regularized generative model to exploit the manifold structure in multiple views. Specifically, we construct a nearest neighbor graph for each view to encode its corresponding manifold information. A multiple graph ensemble regularization framework is proposed to learn the optimal intrinsic manifold. Then, the manifold regularization term is incorporated into a multi-view topic model, resulting in a unified objective function. The solutions are derived based on the Expectation Maximization optimization framework. Experimental results on real-world multi-view data sets demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Tropical Cyclone Intensity Estimation (TIE) is a fundamental study subject for tropical cyclone development, flood or landslide avoidance, etc. Despite considerable efforts, two main challenges remain unresolved in this critical endeavor. The first challenge is that the TIE task is frequently conducted as a coarse-grained recognition problem rather than a fine-grained one. The second challenge is that the prediction fails to consider general wind speed information. To conquer these two challenges, we offer a novel model, namely Tropical cyclone intensity estimation from a Fine-grained perspective with the Graph convolution neural Network (TFG-Net). It is composed of three key components, viz., the Backbone, the Fine-grained Tropical cyclone Features Extractor (FTFE), and the Wind Scale Transition Rule Generator (WTRG), which aim at extracting general spatial features, subtle spatial features, and general wind speed information, respectively. To validate the proposed method, extensive experiments on a well-known real-world tropical dataset named GridSat were carried out. Following the standard benchmark task setting that the model estimates the wind speed from a given satellite image, the proposed TFG-Net reaches 11.12 knots in the RMSE metric, which outperforms 33.33%, 2.54% to the traditional method and the state-of-the-art deep learning method, respectively. The code is available on GitHub: https://github.com/xuguangning1218/TI%5fEstimation and its reproductive result is available on Code Ocean: https://doi.org/10.24433/CO.6606867.v1. • We first proposed to conduct cyclone intensity estimated in fine-grained perspective. • We propose to divide predictive wind speed into general part and particular part. • Extensive experiments and analysis are conducted on the real-world dataset. [ABSTRACT FROM AUTHOR]

• We propose a neural network: ECDNet. It consists of a lightweight two-pathway encoder and an extremely lightweight decoder. • In the encoder, the dense pyramid module (DPM) is designed to have large and diverse receptive fields in feature extraction. • In the encoder, the fusion module (FM) is developed to fuse detail and semantic information more efficiently. • The experiment results on LandSat8 and MODIS demonstrate that ECDNet can achieve state-of-the-art performance. Cloud detection is one of the critical tasks in remote sensing image pre-processing and it has attracted extensive research interest. In recent years, deep neural networks based cloud detection methods have surpassed the traditional methods (threshold-based methods and conventional machine learning-based methods). However, current approaches mainly focus on improving detection accuracy. The computation complexity and large model size are ignored. To tackle this problem, we propose a lightweight deep learning cloud detection model: Efficient Cloud Detection Network (ECDNet). This model is based on the encoder-decoder structure. In the encoder, a two-path architecture is proposed to extract the spatial and semantic information concurrently. One pathway is the detail branch. It is designed to capture low-level detail spatial features with only a few parameters. The other pathway is the semantic branch, which is mainly for capturing context features. In the semantic branch, a proposed dense pyramid module (DPM) is designed for multi-scale contextual information extraction. The number of parameters and calculations in DPM is greatly reduced by features reusing. Besides, a FusionBlock is developed to merge these two kinds of information. Then the extreme lightweight decoder recovers the cloud mask to the same scale as the input image step by step. To improve performance, boost loss is introduced without inference cost increment. We evaluate the proposed method on two public datasets: LandSat8 and MODIS. Extensive experiments demonstrate that the proposed ECDNet achieves comparable accuracy as the state-of-art cloud detection methods, and meantime has a much smaller model size and less computation burden. [ABSTRACT FROM AUTHOR]

• To the best of our knowledge, we are the first to introduce the concept graph and explore the concept hierarchy for addressing the WSFSL problem. • We propose a novel concept graph-based meta-learning framework, consisting of a multi-level conceptual abstraction-based regularization and a meta concept inference network. • Extensive experimental results are reported on two realistic datasets, namely, WS-ImageNet-Pure and WS-ImageNet-Mix, which demonstrate the effectiveness of the proposed framework. In recent years, a large number of meta-learning methods have been proposed to address few-shot learning problems and have shown superior performance. However, the explicit prior knowledge (e.g., concept graph) and weakly-supervised information are rarely explored in existing methods, which are usually free or cheap to collect. In this paper, we introduce a concept graph for the weakly-supervised few-shot learning, and propose a novel meta-learning framework, namely, MetaConcept. Our key idea is to learn a universal meta-learner inferring any-level classifier, so as to boost the classification performance of meta-learning on the novel classes. Specifically, we firstly propose a novel regularization with multi-level conceptual abstraction to train a universal meta-learner to infer not only an entity classifier but also a concept classifier at different levels via the concept graph (i.e., learn to abstract). Then, we propose a meta concept inference network as the universal meta-learner for the base learner, aiming to quickly adapt to a novel task by the joint inference of the abstract concepts and a few annotated samples. We have conducted extensive experiments on two weakly-supervised few-shot learning benchmarks, namely, WS-ImageNet-Pure and WS-ImageNet-Mix. Our experimental results show that (1) the proposed MetaConcept outperforms state-of-the-art methods with an improvement of 2% to 6% in classification accuracy; (2) the proposed MetaConcept is able to yield a good performance though merely training with weakly-labeled datasets. [ABSTRACT FROM AUTHOR]

• We design a discriminative embedding constraint for the heterogeneous domain adaptation problem, which enhances the discriminative power of the common subspace. • To the best of our knowledge, we are the first to integrate the classifier adaptation, distribution alignment, and discriminative embedding constraints into a unified framework. • Many loss (e.g., cross-entropy loss or squared loss) and projection (e.g., linear projection or non-linear projection) functions can be incorporated into the proposed Discriminative Distribution Alignment framework. Two approaches are developed by using the cross-entropy loss and the squared loss, respectively. • Extensive experimental results are reported on the tasks of categorization across domains and modalities, which demonstrate the effectiveness of the proposed Discriminative Distribution Alignment framework. Heterogeneous domain adaptation (HDA) aims to leverage knowledge from a source domain for helping learn an accurate model in a heterogeneous target domain. HDA is exceedingly challenging since the feature spaces of domains are distinct. To tackle this issue, we propose a unified learning framework called Discriminative Distribution Alignment (DDA) for deriving a domain-invariant subspace. The proposed DDA can simultaneously match the discriminative directions of domains, align the distributions across domains, and enhance the separability of data during adaptation. To achieve this, DDA trains an adaptive classifier by both reducing the distribution divergence and enlarging distances between class centroids. Based on the proposed DDA framework, we further develop two methods, by embedding the cross-entropy loss and squared loss into this framework, respectively. We conduct experiments on the tasks of categorization across domains and modalities. Experimental results clearly demonstrate that the proposed DDA outperforms several state-of-the-art models. [ABSTRACT FROM AUTHOR]