Multi-view Multi-label Learning Research Articles

Multi-view Multi-label Learning with Shared Features Inconsistency

Multi-view Multi-label Learning with Shared Features Inconsistency

Deep Double Incomplete Multi-View Multi-Label Learning With Incomplete Labels and Missing Views.

View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery. In the past years, many efforts have been made to address the incomplete multi-view learning or incomplete multi-label learning problem. However, few works can simultaneously handle the challenging case with both the incomplete issues. In this article, we propose a new incomplete multi-view multi-label learning network to address this challenging issue. The proposed method is composed of four major parts: view-specific deep feature extraction network, weighted representation fusion module, classification module, and view-specific deep decoder network. By, respectively, integrating the view missing information and label missing information into the weighted fusion module and classification module, the proposed method can effectively reduce the negative influence caused by two such incomplete issues and sufficiently explore the available data and label information to obtain the most discriminative feature extractor and classifier. Furthermore, our method can be trained in both supervised and semi-supervised manners, which has important implications for flexible deployment. Experimental results on five benchmarks in supervised and semi-supervised cases demonstrate that the proposed method can greatly enhance the classification performance on the difficult incomplete multi-view multi-label classification tasks with missing labels and missing views.

A new multi-view multi-label model with privileged information learning

In multi-view multi-label learning (MVML), the data is described by multiple feature views and annotated by a number of categorical labels. At present, most of the existing MVML methods are proposed based on subspace learning, neural networks and so on. There is little work done on support vector machine (SVM)-based MVML. In this paper, we propose a novel SVM-based multi-view multi-label learning method with privileged information learning (MVMLP). By introducing the idea of privileged information learning, MVMLP implements both the consensus principle and complementarity principle in MVML. Specifically, we enforce similarity between the outputs of different views to explore the consensus information. Moreover, we allow different views to serve as privileged information for each other, such that the complementary information among distinct views can be introduced into the training process. MVMLP constructs an SVM-based model for each view and trains the models of multiple views jointly. The derived learning problem can be efficiently solved by adapting the Franke-Wolfe algorithm. Experimental results on real-life datasets show that MVMLP delivers explicitly better classification performance than state-of-the-art MVML methods.

MVML-MPI: Multi-View Multi-Label Learning for Metabolic Pathway Inference.

Development of robust and effective strategies for synthesizing new compounds, drug targeting and constructing GEnome-scale Metabolic models (GEMs) requires a deep understanding of the underlying biological processes. A critical step in achieving this goal is accurately identifying the categories of pathways in which a compound participated. However, current machine learning-based methods often overlook the multifaceted nature of compounds, resulting in inaccurate pathway predictions. Therefore, we present a novel framework on Multi-View Multi-Label Learning for Metabolic Pathway Inference, hereby named MVML-MPI. First, MVML-MPI learns the distinct compound representations in parallel with corresponding compound encoders to fully extract features. Subsequently, we propose an attention-based mechanism that offers a fusion module to complement these multi-view representations. As a result, MVML-MPI accurately represents and effectively captures the complex relationship between compounds and metabolic pathways and distinguishes itself from current machine learning-based methods. In experiments conducted on the Kyoto Encyclopedia of Genes and Genomes pathways dataset, MVML-MPI outperformed state-of-the-art methods, demonstrating the superiority of MVML-MPI and its potential to utilize the field of metabolic pathway design, which can aid in optimizing drug-like compounds and facilitating the development of GEMs. The code and data underlying this article are freely available at https://github.com/guofei-tju/MVML-MPI. Contact: jtang@cse.sc.edu, guofei@csu.edu.com or wuxi_dyj@csj.uestc.edu.cn.

Within- cross- consensus-view representation-based multi-view multi-label learning with incomplete data

This article develops a multi-view multi-label learning for incomplete data which are ubiquitous with the usage of three kinds of representations including within-view representation, cross-view representation, and consensus-view representation. Different from the recent learning machines, the proposed learning machine takes the feature-oriented information, label-oriented information, and associated information between features and labels in multiple representations together and exploits the hidden useful information of available instances with the usage of instance–instance correlations, feature–feature correlations, label-label correlations, and feature–label correlations. The developed learning machine is named as within- cross- consensus-view representation-based multi-view multi-label learning with incomplete data (WCC-MVML-ID). Extensive experiments on multiple multi-view and multi-label data sets with incomplete data validate the effectiveness of WCC-MVML-ID and it can be concluded that (1) WCC-MVML-ID outperforms other compared learning machines and its performances are more stable even though the missing rates of features and labels being larger; (2) compared with within-view information and consensus-view information, cross-view information is more useful for the processing problem about incomplete data; (3) WCC-MVML-ID can converge within 45 iterations.

Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers

As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view multi-label learning widely applicable for various pattern recognition tasks. In this complex representation learning problem, three main challenges can be characterized as follows: i) How to learn consistent representations of samples across all views? ii) How to exploit and utilize category correlations of multi-label to guide inference? iii) How to avoid the negative impact resulting from the incompleteness of views or labels? To cope with these problems, we propose a general multi-view multi-label learning framework named label-guided masked view- and category-aware transformers in this paper. First, we design two transformer-style based modules for cross-view features aggregation and multi-label classification, respectively. The former aggregates information from different views in the process of extracting view-specific features, and the latter learns subcategory embedding to improve classification performance. Second, considering the imbalance of expressive power among views, an adaptively weighted view fusion module is proposed to obtain view-consistent embedding features. Third, we impose a label manifold constraint in sample-level representation learning to maximize the utilization of supervised information. Last but not least, all the modules are designed under the premise of incomplete views and labels, which makes our method adaptable to arbitrary multi-view and multi-label data. Extensive experiments on five datasets confirm that our method has clear advantages over other state-of-the-art methods.

DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification

In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the uncertain factors of data collection and manual annotation, which means that not only multi-view features are often missing, and label completeness is also difficult to be satisfied. To deal with the double incomplete multi-view multi-label classification problem, we propose a deep instance-level contrastive network, namely DICNet. Different from conventional methods, our DICNet focuses on leveraging deep neural network to exploit the high-level semantic representations of samples rather than shallow-level features. First, we utilize the stacked autoencoders to build an end-to-end multi-view feature extraction framework to learn the view-specific representations of samples. Furthermore, in order to improve the consensus representation ability, we introduce an incomplete instance-level contrastive learning scheme to guide the encoders to better extract the consensus information of multiple views and use a multi-view weighted fusion module to enhance the discrimination of semantic features. Overall, our DICNet is adept in capturing consistent discriminative representations of multi-view multi-label data and avoiding the negative effects of missing views and missing labels. Extensive experiments performed on five datasets validate that our method outperforms other state-of-the-art methods.

A simple multiple-fold correlation-based multi-view multi-label learning

A simple multiple-fold correlation-based multi-view multi-label learning

Non-Aligned Multi-View Multi-Label Classification via Learning View-Specific Labels

In the multi-view multi-label (MVML) classification problem, multiple views are simultaneously associated with multiple semantic representations. Multi-view multi-label learning inevitably has the problems of consistency, diversity, and non-alignment among views and the correlation among labels. Most of the existing multi-view multi-label methods for non-aligned views assume that each view has a common or shared label set, but because a single view cannot contain the entire label information, they often learn suboptimal results. Based on this, this paper proposes a non-aligned multi-view multi-label classification method that learns view-specific labels (LVSL), aiming to explicitly mine the information of view-specific labels and low-rank label structures in non-aligned views in a unified model framework. Furthermore, to alleviate insufficient available label information, we thoroughly explored the global and local structural information among labels. Specifically, first, we assume that there is structural consistency between the view and the label space and then construct the view-specific label model in turn. Second, to enrich the original label space information, we mine the consistent information of multiple views and the low-rank correlation information hidden among multiple labels. Finally, the contribution weight of each view is combined with learning the complementary information among the views in the decision-making stage, and extend the model to handle nonlinear data. The results of the proposed method compared with existing state-of-the-art algorithms on several datasets validate its effectiveness.

Multi-view multi-label learning with high-order label correlation

Multi-label learning deals with a kind of problem that the given samples areassociated with multiple labels simultaneously. Recently, multi-label learning has become a populartopic in the literatures of machine learning and has attracted lots of researches. In this paper, we propose a new multi-view multi-label learning method by considering the label correlation, which is called ELSMML. Based on the high-order strategy, we construct a crafted label correlation matrix to describe the relationships among labels. We further utilize multi-view learning and dimension reduction to exploit the high-level latent semantic label information and the latent feature information, so as to build a classifier in the low dimensional space. In addition, we apply manifold regularization terms to make the data samples in the low dimensional space have the same intrinsic structure as the original data. After that, we put forward the accelerated proximal gradient method to optimize the ELSMML model and obtain thepredictive classifier. Besides, we conduct convergence analysis and computational complexity analysis for ELSMML method. In the experiments, the ELSMML method can achieve better performance on the evaluation metrics compared with other baselines.

Multi-view multi-label learning with double orders manifold preserving

Multi-view multi-label learning with double orders manifold preserving

Label recovery and label correlation co-learning for multi-view multi-label classification with incomplete labels.

Multi-view multi-label learning (MVML) is an important paradigm in machine learning, where each instance is represented by several heterogeneous views and associated with a set of class labels. However, label incompleteness and the ignorance of both the relationships among views and the correlations among labels will cause performance degradation in MVML algorithms. Accordingly, a novel method, label recovery and label correlation co-learning for Multi-View Multi-Label classification with incoMplete Labels (MV2ML), is proposed in this paper. First, a label correlation-guided binary classifier kernel-based is constructed for each label. Then, we adopt the multi-kernel fusion method to effectively fuse the multi-view data by utilizing the individual and complementary information among multiple views and distinguishing the contribution difference of each view. Finally, we propose a collaborative learning strategy that considers the exploitation of asymmetric label correlations, the fusion of multi-view data, the recovery of incomplete label matrix and the construction of the classification model simultaneously. In such a way, the recovery of incomplete label matrix and the learning of label correlations interact and boost each other to guide the training of classifiers. Extensive experimental results demonstrate that MV2ML achieves highly competitive classification performance against state-of-the-art approaches on various real-world multi-view multi-label datasets in terms of six evaluation criteria.

Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning

In multi-view multi-label learning (MVML), each instance is described by several heterogeneous feature representations and associated with multiple valid labels simultaneously. Although diverse MVML methods have been proposed over the last decade, most previous studies focus on leveraging the shared subspace across different views to represent the multi-view consensus information, while it is still an open issue whether such shared subspace representation is necessary when formulating the desired MVML model. In this paper, we propose a DeepGCN based View-Specific MVML method (D-VSM) which can bypass seeking for the shared subspace representation, and instead directly encoding the feature representation of each individual view through the deep GCN to couple with the information derived from the other views. Specifically, we first construct all instances under different feature representations into the corresponding feature graphs respectively, and then integrate them into a unified graph by integrating the different feature representations of each instance. Afterwards, the graph attention mechanism is adopted to aggregate and update all nodes on the unified graph to form structural representation for each instance, where both intra-view correlations and inter-view alignments have been jointly encoded to discover the underlying semantic relations. Finally, we derive a label confidence score for each instance by averaging the label confidence of its different feature representations with the multi-label soft margin loss. Extensive experiments have demonstrated that our proposed method significantly outperforms state-of-the-art methods.

Multi-view multi-label learning with view feature attention allocation

In multi-view multi-label learning, instances can be described by a variety of view features, and they are also associated with a set of labels. Most of the existing multi-view multi-label learning methods extract common and private features from views. However, these methods ignore the fact that the extracted features have different importance for multi-label prediction. To address this issue, this paper proposes a multi-view multi-label learning framework with view feature attention allocation. First, the common and private features between different views are obtained. Then, the original features are compared with the common features to obtain the similarity. Next, the attention weight matrix can be obtained by multiplying the similarity with the synergistic feature matrix. Finally, the acquired attention is used to reconstruct the synergistic feature matrix that indicates the semantic information of the view for multi-label prediction. To verify the effectiveness of the proposed algorithm, experiments are conducted on seven multi-view multi-label datasets, and five advanced algorithms are taken for performance comparison. The extensive experimental results demonstrate the superiority of our algorithm.

Learning view-specific labels and label-feature dependence maximization for multi-view multi-label classification

Multi-view multi-label learning tasks often appear in various critical data classification scenarios. Each training sample has multiple heterogeneous data views associated with multiple labels in this learning framework simultaneously. Nevertheless, most existing methods do not consider that a single view cannot fully predict all unknown labels caused by non-aligned views, which leads to insufficient consideration of the relationship between the features and labels of each view, and the learning effect is not ideal. In this paper, we develop a novel method that uses view-specific labels and label-feature dependence maximization. Concretely, we first assume that each view and its corresponding label space have a smooth local structure. In this way, the view-specific label learning model is constructed, enhancing the consistency and complementarity of label space information. Then, multiple multi-label classifiers are constructed by maximizing label-feature dependence. Finally, the linear classification model is extended to the nonlinear, and the prediction stage is combined with the contribution weight of each view. The results of several benchmark datasets show that our proposed method is significantly more effective than the state-of-the-art methods.

Views Meet Labels: Personalized Relation Refinement Network for Multiview Multilabel Learning

Multiview multilabel (MVML) learning deals with objects with diverse feature vectors and rich semantics. Existing methods are built on a shared latent space among multiple views. However, they do not well capture semantic consistency and view interactions. What’s more, they neglect different contributions of each view to multilabel learning. To address these issues, a novel Personalized RElation rEfinement Network (PREEN) is proposed to sufficiently exploit associated relationships. First, common and specific information is learned in an adversarial way to prevent them from interfering with each other. Then, we adapt the standard transformer to capture cross-view interactions. Similarly, we design label-specific transformers to model label-view dependence, which associates each label with relevant views separately. Eventually, we develop an interlabel attention mechanism to exploit label correlations and refine complementary information from other labels dynamically. Extensive experiments on one real and seven public MVML datasets validate the effectiveness of our proposed PREEN.

Multi-view multi-label active learning with conditional Bernoulli mixtures

Multi-view multi-label active learning with conditional Bernoulli mixtures

Multi-view Multi-label Learning with Incomplete Views and Labels

Multi-view Multi-label Learning with Incomplete Views and Labels

Weak-label-based global and local multi-view multi-label learning with three-way clustering

Weak-label-based global and local multi-view multi-label learning with three-way clustering

Individuality- and Commonality-Based Multiview Multilabel Learning.

In multiview multilabel learning, each object is represented by several heterogeneous feature representations and is also annotated with a set of discrete nonexclusive labels. Previous studies typically focus on capturing the shared latent patterns among multiple views, while not sufficiently considering the diverse characteristics of individual views, which can cause performance degradation. In this article, we propose a novel approach [individuality- and commonality-based multiview multilabel learning (ICM2L)] to explicitly explore the individuality and commonality information of multilabel multiple view data in a unified model. Specifically, a common subspace is learned across different views to capture the shared patterns. Then, multiple individual classifiers are exploited to explore the characteristics of individual views. Next, an ensemble strategy is adopted to make a prediction. Finally, we develop an alternative solution to jointly optimize our model, which can enhance the robustness of the proposed model toward rare labels and reinforce the reciprocal effects of individuality and commonality among heterogeneous views, and thus further improve the performance. Experiments on various real-word datasets validate the effectiveness of ICM2L against the state-of-the-art solutions, and ICM2L can leverage the individuality and commonality information to achieve an improved performance as well as to enhance the robustness toward rare labels.