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High-accuracy GNSS positioning in urban environments is important for applications like safe autonomous driving, however, dynamic errors in complex urban environments limit positioning performances. Recently, deep learning-based (DL) approaches can obtain better GNSS positioning solutions in complex urban environments than model-based ones. However, DL-based approaches simply concentrate one-view GNSS observations as inputs, which are insufficient to model vehicle states accurately, and temporally continuous observations are highly correlated, leading to inaccurate positioning correction results. To solve the challenge, we propose a Sparse Representation-based Multiview Deep Reinforcement Learning model for positioning correction, which employs attention-based multiview fusion to process multiview observations, and uses sparse representation to alleviate disturbances from highly correlated observations. To represent the vehicle state sufficiently, we build a multiview positioning correction environment, and develop an attention-weighted multiview fusion module to fuse temporal features as belief states based on adaptively learned attention weights. To effectively process redundant and correlated multiview features, we impose the ℓ1 norm regularizer to learn sparse hidden representations and improve the precision of value estimation. Finally, we construct a sparse representation-driven multiview actor-critic positioning correction model to achieve high-accuracy GNSS positioning in complex urban environments. We validate performances in both Google Smartphone Decimeter Challenge (GSDC) datasets and our collected GNSS datasets in the Guangzhou area (GZGNSS). Experimental results show that our algorithm can improve localization performances with 27% improvements from WLS+KF in GSDC trajectories, 16% from RTK, and 6% from DL-based methods in GZGNSS trajectories. [ABSTRACT FROM AUTHOR]

In recent years, self-supervised learning-based models have been widely used for electrocardiogram (ECG) representation learning. However, most of the models utilize contrastive learning that strongly depend on data augmentation. In this paper, we propose a masked self-supervised learning model based on multiview information bottleneck principle. Our method masks the ECG signal instances in the time and frequency domains at a high ratio and then uses the autoencoder to reconstruct the original input. Not only the intra-view relations within each view but also the inter-view relations between two views are exploited in ECG representation learning. Furthermore, we use the multiview information bottleneck principle to remove redundant information in the time and frequency domains, so that the representations of both views contain more task-relevant information. Our model is pre-trained on three larger ECG datasets at once and fine-tuned on each classification task. Experimental results show that our model not only outperforms state-of-the-art models with self-supervised learning, but also outperforms models with supervised learning. [ABSTRACT FROM AUTHOR]

Image‐based scheme has attracted wide attention in the fault detection of high‐voltage permanent magnet motors, but it often suffers from the shooting conditions. Multiview feature selection allows multisource information to be fused, which can improve the accuracy and robustness of image detection. Therefore, we propose multiview unsupervised consistency via soft‐label feature selection (MUCSFS). This method constructs consistent pseudo labels through soft labels from clustering affinity of each view sample and builds the model by integrating selection constraints into the graph model. This model filters the fault data set to obtain the feature subset, which is used for clustering. We verify the method's effectiveness by simulating multiview data. The fault clustering experiment on the high‐voltage motors' magnetic tile fault data set confirm that our method can effectively cluster the fault categories. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper addresses the problem of predicting population density leveraging cellular station data. As wireless communication devices are commonly used, cellular station data has become integral for estimating population figures and studying their movement, thereby implying significant contributions to urban planning. However, existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction. To address this, we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data. The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift. Further, we devise a multi-view enhancement model grounded on the Transformer (MVformer), targeting the improvement of the accuracy of extended time-series population predictions. Comparative experiments, conducted on the above-mentioned population dataset using four alternate Transformer-based models, indicate that our proposedMVformer model enhances prediction accuracy by approximately 30% for both univariate and multivariate time-series prediction assignments. The performance of this model in tasks pertaining to population prediction exhibits commendable results. [ABSTRACT FROM AUTHOR]

We present a new restricted SVD-based CUR (RSVD-CUR) factorization for matrix triplets (A, B, G) that aims to extract meaningful information by providing a low-rank approximation of the three matrices using a subset of their rows and columns. The proposed method utilizes the discrete empirical interpolation method (DEIM) to select the subset of rows and columns from the orthogonal and nonsingular matrices obtained through an RSVD of the matrix triplet. We explore the relationships between a DEIM type RSVD-CUR factorization, a DEIM type CUR factorization, and a DEIM type generalized CUR decomposition and provide an error analysis that establishes the accuracy of the RSVD-CUR decomposition within a factor of the approximation error of the RSVD of the given matrices. The RSVD-CUR factorization can be used in applications that require approximating one data matrix relative to two other given matrices. We discuss two such applications, namely multiview dimension reduction and data perturbation problems where a correlated noise matrix is added to the input data matrix. Our numerical experiments demonstrate the advantages of the proposed method over the standard CUR approximation in these scenarios. [ABSTRACT FROM AUTHOR]

The correct prediction of disease-associated miRNAs plays an essential role in disease prevention and treatment. Current computational methods to predict disease-associated miRNAs construct different miRNA views and disease views based on various miRNA properties and disease properties and then integrate the multiviews to predict the relationship between miRNAs and diseases. However, most existing methods ignore the information interaction among the views and the consistency of miRNA features (disease features) across multiple views. This study proposes a computational method based on multiple hypergraph contrastive learning (MHCLMDA) to predict miRNA–disease associations. MHCLMDA first constructs multiple miRNA hypergraphs and disease hypergraphs based on various miRNA similarities and disease similarities and performs hypergraph convolution on each hypergraph to capture higher order interactions between nodes, followed by hypergraph contrastive learning to learn the consistent miRNA feature representation and disease feature representation under different views. Then, a variational auto-encoder is employed to extract the miRNA and disease features in known miRNA–disease association relationships. Finally, MHCLMDA fuses the miRNA and disease features from different views to predict miRNA–disease associations. The parameters of the model are optimized in an end-to-end way. We applied MHCLMDA to the prediction of human miRNA–disease association. The experimental results show that our method performs better than several other state-of-the-art methods in terms of the area under the receiver operating characteristic curve and the area under the precision-recall curve. [ABSTRACT FROM AUTHOR]

Multiview learning has caught the interest of many graph researchers because it can learn richer information about graphs from different views. Recently, multiview learning, as a novel paradigm in learning, has been widely applied to learn nodes representation of heterogeneous graphs, such as MVSE, HeMI, etc., they only utilize the local homogeneous neighborhood information of nodes, which degrades the quality of nodes representation. We are aware that the heterogeneous graph representation aims to drive the representation of a node to be near the homogeneous neighbors that are similar to it in the heterogeneous graph and far wary from heterogeneous neighbors. Besides, in the heterogeneous graph, linked nodes are more likely to be dissimilar, but remote nodes may have some similarities. Therefore, we can move the locality of a node to discover more homogenous neighbors' information to improve the quality of node representation. In this work, we propose an unsupervised heterogeneous graph embedding technique that is simple yet efficient; and devise a systematic way to learn node embeddings from the local and global views of the homogeneous neighborhood of nodes by introducing a regularization framework that minimizes the disagreements among the local and global node embeddings under the specific meta-path. Inspired by Personal PageRank graph diffusion, we expand an infinite meta path-based restart random walk to obtain global homogenous neighbors of nodes and construct a meta path-based diffusion matrix to represent the relation between global homogenous neighbors and nodes. Finally, we employ mini-batch gradient descent to train our model to reduce computational consumption. Experimental findings demonstrate that our approach outperforms a wide variety of baselines on different datasets when it comes to node classification and node clustering tasks, with a particularly impressive 7.22% improvement over the best baseline on the ACM dataset. [ABSTRACT FROM AUTHOR]

Fine-grained urban traffic data are often incomplete owing to limitations in sensor technology and economic cost. However, data-driven traffic analysis methods in intelligent transportation systems (ITSs) heavily rely on the quality of input data. Thus, accurately estimating missing traffic observations is an essential data engineering task in ITSs. The complexity of underlying node-wise correlation structures and various missing scenarios presents a significant challenge in achieving high-precision estimation. This study proposes a novel multiview neural network termed MVCV-Traffic, equipped with a cross-view learning mechanism, to improve traffic estimation. The contributions of this model can be summarized into two parts: multiview learning and cross-view fusing. For multiview learning, several specialized neural networks are adopted to fit diverse correlation structures from different views. For cross-view fusing, a new information fusion strategy merges multiview messages at both feature and output levels to enhance the learning of joint correlations. Experiments on two real-world datasets demonstrate that the proposed model significantly outperforms existing traffic speed estimation methods for different types and rates of missing data. [ABSTRACT FROM AUTHOR]

The accurate prediction of travel demand by bus is crucial for effective urban mobility demand management. However, most models of travel demand prediction by bus tend to focus on the bus's spatiotemporal dependencies, while ignoring the interactions between buses and other transportation modes, such as metros and taxis. We propose a Multiview Spatiotemporal Graph Neural Network (MSTGNN) model to predict short-term travel demand by bus. It emphasizes the ability to capture the interaction dependencies among the travel demand of buses, metros, and taxis. Firstly, a multiview graph consisting of bus, metro, and taxi views is constructed, with each view containing both a local and global graph. Secondly, a multiview attention-based temporal graph convolution module is developed to capture spatiotemporal and cross-view interaction dependencies among different transport modes. Especially, to address the uneven spatial distributions of features in multiview learning, the cross-view spatial feature consistency loss is introduced as an auxiliary loss. Finally, we conduct intensive experiments using a real-world dataset from Shenzhen, China. The results demonstrate that our proposed MSTGNN model performs better than the existing models. Ablation experiments validate the contributions of various modes of transportation to the improvement of the model's performance. [ABSTRACT FROM AUTHOR]

The use of multisource remote sensing data to obtain urban impervious surface has become a popular research topic. Multisource remote sensing data fusion techniques can provide object interpretation with a higher accuracy. However, most decision-level fusion methods make insufficient use of the complementary information and degree of association between similar object data. To fill this gap, in this article, we propose a dual-view learning fusion classification method (DvLF) based on multiview learning. First, DvLF uses cotraining algorithm to combine multiple data sources for accurate classification, extracting easy-to-classify area while separating difficult-to-classify regions for further analysis. Second, a canonical correlation analysis method is adopted to mine the degree of association of similar object data for constructing a subspace projection field of each object sample. The data in the difficult-to-classify regions are classified in the projection field of each object, and then the results of each classification are fused by voting. Finally, the classification results of the two regions are combined into the classification results of the whole image to achieve impervious surface mapping. The proposed method is applied to the dual-sensor (high-resolution image and LiDAR) Buffalo dataset and the dual-sensor (RGB and multispectral LiDAR) Houston dataset. The experimental results show that our method achieved a significant improvement in classification accuracy compared to other methods. The overall classification accuracy of this new DvLF fusion method on the Buffalo and Houston datasets is 83.35% and 88.84%, respectively, leading to accurate high-resolution impervious surface mapping.

Graphs have a crucial impact on the performance of any graph-based semi-supervised learning method, so their construction should be carefully considered. In this letter, and in the context of semi-supervised learning, we will address graph-based semi-supervised learning employing multiple views for the data. The notion of data smoothness is another missing concept in graph construction that should be considered when constructing graphs. Compared to a single feature, using multiple sources of information can increase the efficiency of the post-processing task that adopts the constructed graph. Therefore, we present an approach that merges the notions of data smoothness and label smoothness with label fitness and projection matrix calculation. Moreover, two or more views are merged to exploit the information hidden in different features. Experiments performed with image databases show the superiority of the proposed approach compared to single features and other competing fusion algorithms. Compared to recent fusion methods, the introduced scheme improved the semi-supervised classification performance. For example, on the MNIST dataset with 20 labeled images per class, the average improvement due to the proposed labeling inference was 4.4%. The proposed method is inductive and computes a linear mapping to obtain the label of unseen or test patterns. [ABSTRACT FROM AUTHOR]

Palmprint recognition has been widely applied to security authentication due to its rich characteristics, i.e., local direction, wrinkle, and texture. However, different types of palmprint images captured from different application scenarios usually contain a variety of dominant features. Specifically, the palmprint recognition performance will be degraded by the interference factors, i.e., noise, rotations, and shadows, while palmprint images are acquired in the open-set environments. Seeking to handle the long-standing interference information in the images, multiview palmprint feature learning has been proposed to enhance the feature expression by exploiting multiple characteristics from diverse views. In this paper, we first introduced six types of palmprint representation methods published from 2004 to 2022, which described the characteristics of palmprints from a single view. Afterward, a number of multiview-learning-based palmprint recognition methods (2004–2022) were listed, which discussed how to achieve better recognition performances by adopting different complementary types of features from multiple views. To date, there is no work to summarize the multiview fusion for different types of palmprint features. In this paper, the aims, frameworks, and related methods of multiview palmprint representation will be summarized in detail. [ABSTRACT FROM AUTHOR]

BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value

Abstract Background In Alzheimer’s Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities. Results DGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify. Conclusion Overall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI.

In this article, we focus on the unsupervised multiview feature selection, which tries to handle high-dimensional data in the field of multiview learning. Although some graph-based methods have achieved satisfactory performance, they ignore the underlying data structure across different views. Besides, their predefined Laplacian graphs are sensitive to the noises in the original data space and fail to obtain the optimal neighbor assignment. To address the above problems, we propose a novel unsupervised multiview feature selection model based on graph learning, and the contributions are three-fold: 1) during the feature selection procedure, the consensus similarity graph shared by different views is learned. Therefore, the proposed model can reveal the data relationship from the feature subset; 2) a reasonable rank constraint is added to optimize the similarity matrix to obtain more accurate information; and 3) an autoweighted framework is presented to assign view weights adaptively, and an effective alternative iterative algorithm is proposed to optimize the problem. Experiments on various datasets demonstrate the superiority of the proposed method compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Semisupervised multiview learning gains extensive research attention due to its strong capability to utilize the heterogeneous features and the label information of a few labeled samples. However, the supervision information is not well utilized in the process of exploring the consensus structure of the multiview data. In this paper, we propose a novel unified pseudolabel‐guided multiview consensus (PMvC) learning framework for the semisupervised classification problem, which learns the consensus structure of multiview data by fully exploiting the supervised information of labeled samples. Specifically, PMvC first assigns multiple pseudolabels to the unlabeled samples by selecting the nearest labeled sample in each view separately, and then labels the part of unlabeled samples by selecting the pseudolabel that agrees across all views. By doing so, the high‐confident pseudolabeled samples can be selected to enlarge the labeled sample pool and the supervision information can be exploited further in the learning process. In addition, to capture the consensus structure of the multiview data, PMvC learns a consensus graph from the view‐specific self‐representation graph guided by enhanced supervision information, which better preserves the manifold structure of samples. Meanwhile, the label information is also propagated from the labeled samples to the unlabeled samples by the learned consensus graph simultaneously. Accordingly, an effective optimization algorithm is derived to find the optimal solution for PMvC. Extensive experiment results on several real‐world data sets demonstrate the feasibility and superiority of PMvC. The source code of PMvC is available at https://github.com/justcallmewilliam/PMvC. [ABSTRACT FROM AUTHOR]

Multiview clustering aims to leverage information from multiple views to improve the clustering performance. Most previous works assumed that each view has complete data. However, in real-world datasets, it is often the case that a view may contain some missing data, resulting in the problem of incomplete multiview clustering (IMC). Previous approaches to this problem have at least one of the following drawbacks: 1) employing shallow models, which cannot well handle the dependence and discrepancy among different views; 2) ignoring the hidden information of the missing data; and 3) being dedicated to the two-view case. To eliminate all these drawbacks, in this work, we present the adversarial IMC (AIMC) framework. In particular, AIMC seeks the common latent representation of multiview data for reconstructing raw data and inferring missing data. The elementwise reconstruction and the generative adversarial network are integrated to evaluate the reconstruction. They aim to capture the overall structure and get a deeper semantic understanding, respectively. Moreover, the clustering loss is designed to obtain a better clustering structure. We explore two variants of AIMC, namely: 1) autoencoder-based AIMC (AAIMC) and 2) generalized AIMC (GAIMC), with different strategies to obtain the multiview common representation. Experiments conducted on six real-world datasets show that AAIMC and GAIMC perform well and outperform the baseline methods. [ABSTRACT FROM AUTHOR]

Background: In Alzheimer's Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities. Results: DGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify. Conclusion: Overall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI. [ABSTRACT FROM AUTHOR]

Sagittal craniosynostosis (CSO) occurs when the sagittal suture of a growing child's skull is fused prematurely. Surgery, which involves re-moving the affected bones and increasing the volume of the cranium by repositioning the bone segments or using external forces to guide, is the primary treatment for CSO. For preparation of the surgery, physicians usually classify sagittal CSO subtypes by examining the reconstruction of skulls from CT images or the laser scans of the patients' 3D skull. We have proposed an automated sagittal CSO classification algorithm based on features extracted from projected 3D skull images. However, these features would become invalid if they were extracted from irregular skulls or skulls with completely closed sutures. In order to tackle this problem, we proposed a 3D skull multi-view images-based algorithm to classify the subtypes of sagittal CSO, like the physicians diagnosing these 3D reconstruction images of skulls or laser scans. Our innovations include: 1) the first to propose a multi-view learning algorithm to classify the subtypes of sagittal CSO cases, 2) 3D rendering and a patch-based erosion method are adopted for data augmentation to keep the original ratio and shape of the images, and 3) utilizing a transfer leaning training strategy to train the convolutional neural network (CNN) and a multi-view-based prediction strategy for classifying the cases. [ABSTRACT FROM AUTHOR]

Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: they encounter the expensive time overhead, they fail to explore the explicit clusters, and cannot generalize to unseen data points. In this work, we propose a scalable graph learning framework, seeking to address the above three challenges simultaneously. Specifically, it is based on the ideas of anchor points and bipartite graph. Rather than building an $n\times n$ graph, where $n$ is the number of samples, we construct a bipartite graph to depict the relationship between samples and anchor points. Meanwhile, a connectivity constraint is employed to ensure that the connected components indicate clusters directly. We further establish the connection between our method and the $K$ -means clustering. Moreover, a model to process multiview data is also proposed, which is linearly scaled with respect to $n$. Extensive experiments demonstrate the efficiency and effectiveness of our approach with respect to many state-of-the-art clustering methods. [ABSTRACT FROM AUTHOR]

The aspect-based sentiment analysis (ABSA) consists of two subtasks'aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies some problems in performance and real application. This study investigates the end-to-end ABSA and proposes a novel multitask multiview network (MTMVN) architecture. Specifically, the architecture takes the unified ABSA as the main task with the two subtasks as auxiliary tasks. Meanwhile, the representation obtained from the branch network of the main task is regarded as the global view, whereas the representations of the two subtasks are considered two local views with different emphases. Through multitask learning, the main task can be facilitated by additional accurate aspect boundary information and sentiment polarity information. By enhancing the correlations between the views under the idea of multiview learning, the representation of the global view can be optimized to improve the overall performance of the model. The experimental results on three benchmark datasets show that the proposed method exceeds the existing pipeline methods and end-to-end methods, proving the superiority of our MTMVN architecture.

Monocular image-based 3-D model retrieval aims to search for relevant 3-D models from a dataset given one RGB image captured in the real world, which can significantly benefit several applications, such as self-service checkout, online shopping, etc. To help advance this promising yet challenging research topic, we built a novel dataset and organized the first international contest for monocular image-based 3-D model retrieval. Moreover, we conduct a thorough analysis of the state-of-the-art methods. Existing methods can be classified into supervised and unsupervised methods. The supervised methods can be analyzed based on several important aspects, such as the strategies of domain adaptation, view fusion, loss function, and similarity measure. The unsupervised methods focus on solving this problem with unlabeled data and domain adaptation. Seven popular metrics are employed to evaluate the performance, and accordingly, we provide a thorough analysis and guidance for future work. To the best of our knowledge, this is the first benchmark for monocular image-based 3-D model retrieval, which aims to help related research in multiview feature learning, domain adaptation, and information retrieval. [ABSTRACT FROM AUTHOR]

Due to the unique characteristics of remote sensing (RS) data, it is challenging to collect richer labeled samples for training the deep learning model compared with the natural image data. To solve this problem, recently, multisource-single-target (MS $^{2}\text{T}$) scenarios have started receiving significant attention in which the knowledge from multiple sources is integrated to transfer to a target domain with the assumption that label spaces of each source and target domain are the same. However, in real-world applications, it can be challenging to find a source domain that completely includes all classes of the target domain. Therefore, to cover all class information of the target domain, they often naïvely merge multiple sources into a complete single source. However, each source domain typically has a different data distribution; thus, the semantic information of each source domain can be damaged, leading to degrading the classification accuracy of the target domain. To address this problem, we propose a unified framework termed multiexpert collaboration for knowledge adaptation (MECKA) from various sources. MECKA includes two main processes: multiple-view generation and collaborative learning. Multiview learning plays an essential role in preserving the unique characteristics of each source domain. In contrast, collaborative learning is responsible for connecting these views that leverage complementary information from each other to perform on an unseen target domain robustly. Experimental results showed that the proposed method achieved the best classification accuracy on RS scene benchmark datasets on both complete and incomplete multisource unsupervised domain adaptation (UDA) tasks compared to benchmark methods. [ABSTRACT FROM AUTHOR]

The difficulties of obtaining sufficient labeled samples have always been one of the factors hindering deep learning models from obtaining high accuracy in hyperspectral image (HSI) classification. To reduce the dependence of deep learning models on training samples, meta learning methods have been introduced, effectively improving the classification accuracy in small sample set scenarios. However, the existing methods based on meta learning still need to construct a labeled source data set with several pre-collected HSIs, and must utilize a large number of labeled samples for meta-training, which is actually time-consuming and labor-intensive. To solve this problem, this paper proposes a novel unsupervised meta learning method with multiview constraints for HSI small sample set classification. Specifically, the proposed method first builds an unlabeled source data set using unlabeled HSIs. Then, multiple spatial-spectral multiview features of each unlabeled sample are generated to construct tasks for unsupervised meta learning. Finally, the designed residual relation network is used for meta-training and small sample set classification based on the voting strategy. Compared with existing supervised meta learning methods for HSI classification, our method can only utilize HSIs without any label for unsupervised meta learning, which significantly reduces the number of requisite labeled samples in the whole classification process. To verify the effectiveness of the proposed method, extensive experiments are carried out on 8 public HSIs in the cross-domain and in-domain classification scenarios. The statistical results demonstrate that, compared with existing supervised meta learning methods and other advanced classification models, the proposed method can achieve competitive or better classification performance in small sample set scenarios. [ABSTRACT FROM AUTHOR]

Palmprint recognition has been widely used in security authentication. However, most of the existing palmprint representation methods are focused on a special application scenario using the hand-crafted features from a single-view. If the features become weak as the application scenario changes, the recognition performance will be degraded. To address this problem, we propose to comprehensively exploit palmprint features from multiple views to improve the recognition performance in generic scenarios. In this paper, a novel double-cohesion learning based multiview and discriminant palmprint recognition (DC_MDPR) method is proposed, which imposes a double-cohesion strategy to reduce the inter-view margins for each subject and the intra-class margins for each view. In this way, for each subject, the features from different views can be closer to each other in the binary-label space. Meanwhile, for each view, the features sharing the same label information can move towards each other by imposing a neighbor graph regularization. The proposed method can be flexibly applied to any type of palmprint feature fusion. Moreover, it presents the multiview features in a low-dimensionality sub-space, effectively reducing the computational complexity. Experimental results on various palmprint databases have shown that the proposed method can always achieve the best recognition performance compared to other state-of-the-art algorithms. • We proposed a novel DC_MDPR method for discriminative palmprint recognition. • A double-cohesion strategy is proposed to explore multiview palmprint information. • Inter-view cohesion is utilized to reduce margins of features from different views. • Intra-class cohesion is adopted to reduce margins among the intra-class features. • Proposed method outperformed the other state-of-the-art methods in the experiments. [ABSTRACT FROM AUTHOR]

This article shows a bibliographic review of the academic literature related to "Image classification with Multiview learning" together with an analysis of the information present in each of the reviewed bibliographic sources, to propose a conceptual basis, theoretical and statistical for research works that develop or contain this theme. In the same way, the way in which the MVL is approached in the different application scenarios, both academic and practical, is briefly presented. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Convolutional neural network (CNN) has been proven an effective way to extract high-level features from remote sensing (RS) images automatically. Many variants of the CNN model have been proposed, including principal component analysis network (PCANet), canonical correlation analysis network (CCANet), multiple scale CCANet (MS-CCANet) and multiview CCANet (MCCANet). The PCANet is specialized for single view feature abstraction, while in many real-world practices, the RS data are frequently observed from many more views. Although CCANet, MS-CCANet and MCCANet can be applied to two or more view data, they consider only the pair-wise correlation by calculating a series of two-order covariance matrices. However, the high-order consistence, which can only be explored by collectively and simultaneously examining all views, remains undiscovered. In this paper, we propose the tensor canonical correlation analysis network (TCCANet) to tackle this problem. Particularly, TCCANet learns filter banks by simultaneously maximizing arbitrary number of views with high-order-correlation and solves the optimization problem by decomposing a covariance tensor. After the convolutional stage, we utilize binarization and block-wise histogram strategies to generate the final feature. Furthermore, we also develop a Multiple Scale version of TCCANet, i.e., MS-TCCANet, to extract enriched representation of the RS data by incorporating all previous convolutional layers. Numerical experiment results on RSSCN7 and SAT-6 datasets demonstrate the advantages of TCCANet and MS-TCCANet for RS scene recognition. [ABSTRACT FROM AUTHOR]

With the development of the imaging technology of various sensors, multisource image classification has become a key challenge in the field of image interpretation. In this article, a novel classification method, called the deep multiview union learning network (DMULN), is proposed to classify multisensor data. First, an associated feature extractor is designed to process the multisource data by canonical correlation analysis (CCA) in the head of the network. Second, an improved deep learning architecture with two branches is presented to extract high-level view features from the associated features. Third, a novel pooling, called view union pooling, is proposed to fuse the multiview feature from the deep model. Finally, the fused feature is fed into the classifier. The proposed framework is easy to optimize since it is an end-to-end network. Extensive experiments and analysis on the datasets $IEEE\_{}grss\_{}dfc\_{}2017$ and $IEEE\_{}grss\_{}dfc\_{}2018$ show that the proposed method achieves comparable results. Our results demonstrate that abundant multisource information can improve the classification performance. [ABSTRACT FROM AUTHOR]

Cell-penetrating peptides (CPPs) are short peptides that can carry cargo into cells. CPPs are widely utilized due to their powerful loading capacity and transduction efficiency. Identifying CPPs is the basis for studying their functions and mechanisms; however, experimental methods to identify CPPs are expensive and time-consuming. Recently, CPP predictors based on machine learning methods have become a research hotspot. Although considerable progress has been made, some challenges remain unresolved. First, most predictors employ a variety of feature descriptors to transform an original sequence into multiview data; however, extant methods ignore the relationships between different views, limiting further performance improvement. Second, most machine learning models are actually black boxes and cannot offer insightful advice. In this paper, a novel Hilbert–Schmidt independence criterion (HSIC)-based multiview TSK fuzzy system is proposed. Compared with other machine learning methods, TSK fuzzy systems have better interpretability, and the introduction of multiview mechanisms provides comprehensive insight into the intrinsic laws of the data. HSIC is utilized here to measure the independence and enhance the complementarity between different views. Notably, the proposed method attained prediction accuracy results of 92.2% and 96.2% for the training and independent test sets, respectively. The empirical results show that our promising approach features greater recognition performance than the state-of-the-art method. [ABSTRACT FROM AUTHOR]

Multiview learning (MVL) seeks to leverage the benefits of diverse perspectives to complement each other, effectively extracting and utilizing the latent information within the dataset. Several twin support vector machine-based MVL (MvTSVM) models have been introduced and demonstrated outstanding performance in various learning tasks. However, MvTSVM-based models face significant challenges in the form of computational complexity due to four matrix inversions, the need to reformulate optimization problems in order to employ kernel-generated surfaces for handling non-linear cases, and the constraint of uniform noise assumption in the training data. Particularly in cases where the data possesses a heteroscedastic error structure, these challenges become even more pronounced. In view of the aforementioned challenges, we propose multiview twin parametric margin support vector machine (MvTPMSVM). MvTPMSVM constructs parametric margin hyperplanes corresponding to both classes, aiming to regulate and manage the impact of the heteroscedastic noise structure existing within the data. The proposed MvTPMSVM model avoids the explicit computation of matrix inversions in the dual formulation, leading to enhanced computational efficiency. We perform an extensive assessment of the MvTPMSVM model using benchmark datasets such as UCI, KEEL, synthetic, and Animals with Attributes (AwA). Our experimental results, coupled with rigorous statistical analyses, confirm the superior generalization capabilities of the proposed MvTPMSVM model compared to the baseline models. The source code of the proposed MvTPMSVM model is available at https://github.com/mtanveer1/MvTPMSVM., Competing Interests: Declaration of competing interest The authors of this work have no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Palmprint recognition has been widely applied to security authentication due to its rich characteristics, i.e., local direction, wrinkle, and texture. However, different types of palmprint images captured from different application scenarios usually contain a variety of dominant features. Specifically, the palmprint recognition performance will be degraded by the interference factors, i.e., noise, rotations, and shadows, while palmprint images are acquired in the open-set environments. Seeking to handle the long-standing interference information in the images, multiview palmprint feature learning has been proposed to enhance the feature expression by exploiting multiple characteristics from diverse views. In this paper, we first introduced six types of palmprint representation methods published from 2004 to 2022, which described the characteristics of palmprints from a single view. Afterward, a number of multiview-learning-based palmprint recognition methods (2004–2022) were listed, which discussed how to achieve better recognition performances by adopting different complementary types of features from multiple views. To date, there is no work to summarize the multiview fusion for different types of palmprint features. In this paper, the aims, frameworks, and related methods of multiview palmprint representation will be summarized in detail.

Diagnosis of non-tumorous facial pigmentation is an important and difficult issue in the field of dermatology (For example, chloasma and freckles are too similar to distinguish, but they often appear at the same time). In this paper, different from other studies, we used images with nine different forms (views) to comprehensively diagnose these diseases for the first time. In detail, firstly, we collected and published the first multi-view non-tumorous facial pigmentation (MVNFP) dataset provided as the benchmark for classification and segmentation problems: MVNFP, where each face consists of 9 images: 1) Standard Image, 2) Polarization Image, 3) Oil Secretion Image, 4) Sunshine Damage Image, 5) Pigmentation Image, 6) Physiological Aging Image, 7) Sensitive Image, 8) Brown Image, 9) Pigmentation Trend Image. MVNFP has a total of 10,719 images, 5 categories which brings great convenience for the follow-up research. Secondly, to make full use of the informations behind these images described from different views, we use multi-view convolutional neural network to diagnose these indistinguishable diseases. In addition, we introduced the attention mechanisms, and at the same time deeply studied the many influencing factors of multi-view non-tumorous facial pigmentation diagnosis. All experimental results show our method has achieved state-of-the-art results on the MVNFP dataset. Code and MVNFP dataset are released at: https://github.com/sunshiding/MVNFP.git. [ABSTRACT FROM AUTHOR]

Due to emerging development of intelligent sensing technologies in Internet of Things, multisensor cooperation has been widely deployed in applications. Although multisensor information fusion can be addressed by multiview learning, its performance tends to degrade if any one sensor is disturbed with annoying noises by the environment or other factors. Therefore, fusing these cross-sensor data in a reliable and secure manner while removing those noises is crucial. Although there have been outlier-against multiview works proposed, most of them suffer from redundant parameters or performance degradation. Even worse, few of them have considered the complementary information across the sensors. In this article, we argue that in multiview information fusion, not only the clean data, but also those outliers share the same prototypes in a common space, except that the outliers are disturbed with noises. To this end, we propose a type of robust multiview prototype (RMVP) learning to fuse the sensing data while removing the noises automatically in the learning process. Specifically, in RMVP, projection matrices are designed for each sensing view to sketch the data prototypes. In addition, one auxiliary margin matrix is modeled for each sensing view to capture its data noises through penalizing a sparsity regularization on it. Afterwards, an alternating algorithm is presented to solve the proposed model. Finally, extensive experiments on intelligent sensing data sets are conducted to testify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In many computer vision applications, an object can be represented by multiple different views. Due to the heterogeneous gap triggered by the different views’ inconsistent distributions, it is challenging to exploit these multiview data for cross-view retrieval and classification. Motivated by the fact that both labeled and unlabeled data can enhance the relations among different views, this article proposes a deep cross-view learning framework called deep semisupervised classes- and correlation-collapsed cross-view learning (DSC3L) for cross-view retrieval and classification. Different from the existing methods which focus on the two-view problems, the proposed method learns $U$ (generally $U\geq 2$) view-specific deep transformations to gradually project $U$ different views into a shared space in which the projection embraces the supervised learning and the unsupervised learning. We propose collapsing the instances of the same class from all views into the same point, with the instances of different classes into distinct points simultaneously. Second, to exploit the abundant unlabeled $U$ -wise multiview data, we propose to collapse-correlated data into the same point, with the uncorrelated data into distinct points. Specifically, these two processes are formulated to minimize the two Kullback-Leibler (KL) divergences between the conditional distribution and a desirable one, for each instance. Finally, the two KL divergences are integrated into a joint optimization to learn a discriminative shared space. The experimental results on five widely used public datasets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The authors develop a decision support system for design and branding based on a multimodal variational autoencoder that merges image, text, and ratings data. Logos serve a fundamental role as the visual figureheads of brands. Yet, because of the difficulty of using unstructured image data, prior research on logo design has largely been limited to nonquantitative studies. In this work, we explore the interplay between logo design and brand identity creation from a data-driven perspective. We develop both a novel logo feature extraction algorithm that uses modern image processing tools to decompose pixel-level image data into meaningful features and a multiview representation learning framework that links these visual features to textual descriptions, consumer ratings of brand personality, and other high-level tags describing firms. We apply this framework to a unique data set of brands to understand which brands use which logo features and how consumers evaluate these brands' personalities. Moreover, we show that manipulating the model's learned representations through what we term "brand arithmetic" yields new brand identities and can help with ideation. Finally, through an application to fast-food branding, we show how our model can be used as a decision support tool for suggesting typical logo features for a brand and for predicting consumers' reactions to new brands or rebranding efforts. [ABSTRACT FROM AUTHOR]

Significant advances in graph-oriented clustering methods can be attributed to their effectiveness in leveraging relationships and complex structures within multiview data. However, several limitations persist in most existing graph-based multiview clustering approaches. First, quadratic or cubic complexity is required for graph construction or eigendecomposition of the Laplacian matrix in many existing methods. Second, certain methods overlook the differences between views and employ an identical indicator matrix, which can lead to over-learning in practical scenarios. Third, existing methods often neglect spatial structure and complementary information, focusing primarily on calculating error feature-by-feature using different norms. In order to tackle these drawbacks, we propose a new multiview spectral clustering model called A nchor G raph-based M ultiview S pectral C lustering(AG-MSC). AG-MSC incorporates an adaptive weighting mechanism that assigns weights to each view, enhancing the robustness of the algorithm. Using a tensor Schatten p -norm constraint minimizes the discrepancy between indicator matrices obtained from different views, thereby preserving high-order information and spatial structure. To improve computational efficiency, we replace the full adjacency matrices of the corresponding views with anchor graphs. AG-MSC offers a distinct advantage over conventional spectral clustering by directly obtaining all sample categories without additional post-processing steps. We have validated the efficiency of our method through extensive experimental evaluations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel algorithm called multiview temporal alignment by dependence maximisation in the latent space (TRANSIENCE) for the alignment of time series consisting of sequences of feature vectors with different length and dimensionality of the feature vectors. The proposed algorithm, which is based on the theory of multiview learning, can be seen as an extension of the well-known dynamic time warping (DTW) algorithm but, as mentioned, it allows the sequences to have different dimensionalities. Our algorithm attempts to find an optimal temporal alignment between pairs of nonaligned sequences by first projecting their feature vectors into a common latent space where both views are maximally similar. To do this, powerful, nonlinear deep neural network (DNN) models are employed. Then, the resulting sequences of embedding vectors are aligned using DTW. Finally, the alignment paths obtained in the previous step are applied to the original sequences to align them. In the paper, we explore several variants of the algorithm that mainly differ in the way the DNNs are trained. We evaluated the proposed algorithm on a articulatory-to-acoustic (A2A) synthesis task involving the generation of audible speech from motion data captured from the lips and tongue of healthy speakers using a technique known as permanent magnet articulography (PMA). In this task, our algorithm is applied during the training stage to align pairs of nonaligned speech and PMA recordings that are later used to train DNNs able to synthesis speech from PMA data. Our results show the quality of speech generated in the nonaligned scenario is comparable to that obtained in the parallel scenario. [ABSTRACT FROM AUTHOR]

Multiple kernel clustering (MKC) methods aim at integrating an optimal partition from a set of precalculated kernel matrices. Though achieving success in various applications, we observe that existing MKC methods: (i) lack of representation flexibility; and (ii) do not considerably preserve the locality structure in partition space. These issues may adversely affect the learning procedure of MKC, leading to unsatisfying clustering performance. In this paper, we propose a late fusion MKC method with local graph refinement to address the aforementioned issues. Different from existing MKC mechanisms, our method unifies the traditional weighted multiple kernel k‐means, kernel partition, and graph construction into a single optimization procedure. The local graph is utilized to preserve the locality information in partition space and therefore all of the counterparts can be boosted for mutual clustering improvements. By this way, our approach enhances the local graph structure in partition space and enjoys more flexible kernel representations, leading to significant clustering improvements. Moreover, a three‐step alternate algorithm is developed to solve the resultant optimization problem with proved convergence. Extensive experiments are conducted on several multiple kernel benchmark datasets to compare the proposed algorithm with the state‐of‐the‐art ones, and the results well demonstrate its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

A key objective in multiview learning is to model the information common to multiple parallel views of a class of objects/events to improve downstream tasks such as classification and clustering. In this context, two open research challenges remain; achieving scalability: how can we incorporate information from hundreds of views per event into a model? and being view-agnostic: how to learn robust multiview representations without knowledge of how these views are acquired? In this work, we study a neural method based on multiview correlation to capture the information shared across a large number of views by subsampling them in a view-agnostic manner during training. We analyze the error of this bootstrapped multiview correlation objective using matrix concentration theory to provide an upper bound on the number of views to subsample for a given embedding dimension. Our experiments on a diverse set of audio and visual tasks—multi-channel acoustic activity classification, spoken word recognition, 3D object classification, and pose-invariant face recognition—demonstrate the robustness of view bootstrapping to model a large number of views. Results and analysis underscore the applicability of our method for a view-agnostic learning setting. [ABSTRACT FROM AUTHOR]

Considering the impact of high dimensional data redundancy and noise interference on multiview subspace clustering, a robust multiview subspace clustering method based on multi-kernel low redundancy representation learning was proposed. Firstly, by analyzing and revealing the redundancy and noise influence characteristics of data in kernel space, a multi-kernel learning method was proposed to obtain a robust low-redundancy representation of local view-specific data, which was utilized to replace the original data to implement subspace learning. Secondly, a tensor analysis model was introduced to carry out multiview fusion, so as to learn the potential low-rank tensor structure among different subspace representations from global perspective. It would capture the high-order correlation among views while maintaining their unique information. In this method, robust low-redundancy representation learning, view-specific subspace learning and fusion potential subspace structure learning were unified into the same objective function, so that they could promote each other during iterations. A large number of experimental results demonstrate that the proposed method is superior to the existing mainstream multiview clustering methods on several objective evaluation indicators. [ABSTRACT FROM AUTHOR]