Multi-view Multi-label Learning Research Articles

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

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

Identification of Essential Proteins Based on Local Random Walk and Adaptive Multi-View Multi-Label Learning.

Accumulating evidences have indicated that essential proteins play vital roles in human physiological process. In recent years, although researches on prediction of essential proteins have been developing rapidly, there are as well various limitations such as unsatisfactory data suitability, low accuracy of predictive results and so on. In this manuscript, a novel method called RWAMVL was proposed to predict essential proteins based on the Random Walk and the Adaptive Multi-View multi-label Learning. In RWAMVL, considering that the inherent noise is ubiquitous in existing datasets of known protein-protein interactions (PPIs), a variety of different features including biological features of proteins and topological features of PPI networks were obtained by adopting adaptive multi-view multi-label learning first. And then, an improved random walk method was designed to detect essential proteins based on these different features. Finally, in order to verify the predictive performance of RWAMVL, intensive experiments were done to compare it with multiple state-of-the-art predictive methods under different expeditionary frameworks. And as a result, RWAMVL was proven that it can achieve better prediction accuracy than all those competitive methods, which demonstrated as well that RWAMVL may be a potential tool for prediction of key proteins in the future.

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.

Consistency and diversity neural network multi-view multi-label learning

In multi-view multi-label learning, each object is represented by multiple heterogeneous data and is simultaneously associated with multiple class labels. Previous studies usually use shared subspaces to fuse multi-view representations. However, as the number of views increases, it is more challenging to capture the high-order relationships among multiple views. Therefore, a novel neural network multi-view multi-label learning framework is proposed, which is intended to solve the problem of consistency and diversity among views through a simple and effective method(CDMM). First, we build a separate classifier for each view based on the neural network method of the nonlinear kernel mapping function and require each view to learn a consistent label result. Then, we consider the diversity of individual views while learning a consistent representation among views. For this reason, we combine the Hilbert–Schmidt Independence Criterion with exploring the diversity among different views. Finally, the label correlation factor is in addition to the classification model, and the view contribution factor is added to the prediction model. A large number of comparative experiments with existing state-of-the-art solutions on benchmark multi-view multi-label learning data sets show the effectiveness of this method.

Global and local multi-view multi-label learning with incomplete views and labels

Multi-view multi-label learning is widely used in multiple fields, and it aims to process data sets represented by multiple forms (views) and labeled by multiple classes. But most real-world data sets maybe loss some labels and views due to lack of manpower and equipment failure and this causes some difficulties in processing data sets. In this paper, we develop a global and local multi-view multi-label learning with incomplete views and labels (GLMVML-IVL) to process this. In GLMVML-IVL, the usage of label-specific features indicates that class label is determined by some specific features rather than all features; global and local label correlations are taken into consideration with clustering technology; the construction of the pseudo-class label matrix offsets the defect of missing (partial) labels; the adoption of low-rank assumption matrix restores incomplete views; a consensus multi-view representation is put to use to encode the complementary information from different views; the regularizer imposed on label matrix reflects the partial pairwise constraints. Different from traditional methods, this is the first attempt to design a multi-view multi-label learning method with incomplete views and labels by the learning of label-specific features, pseudo-class label matrix, low-rank assumption matrix, global and local label correlations, complementary information, and regularizer imposed on label matrix. Experimental results validate that GLMVML-IVL improves the performance of traditional multi-view multi-label learning methods in statistical and achieves a better performance.

Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation

In multi-view multi-label learning (MVML), each training example is represented by different feature vectors and associated with multiple labels simultaneously. Nonetheless, the labeling quality of training examples is tend to be affected by annotation noises. In this paper, the problem of multi-view partial multi-label learning (MVPML) is studied, where the set of associated labels are assumed to be candidate ones and only partially valid. To solve the MVPML problem, a two-stage graph-based disambiguation approach is proposed. Firstly, the ground-truth labels of each training example are estimated by disambiguating the candidate labels with fused similarity graph. After that, the predictive model for each label is learned from embedding features generated from disambiguation-guided clustering analysis. Extensive experimental studies clearly validate the effectiveness of the proposed approach in solving the MVPML problem.

Global and local multi-view multi-label learning

In order to process multi-view multi-label data sets, we propose global and local multi-view multi-label learning (GLMVML). This method can exploit global and local label correlations of both the whole data set and each view simultaneously. What’s more, GLMVML introduces a consensus multi-view representation which encodes the complementary information from different views. Related experiments on three multi-view data sets, fourteen multi-label data sets, and one multi-view multi-label data set have validated that (1) GLMVML has a better average AUC and precision and it is superior to the classical multi-view learning methods and multi-label learning methods in statistical; (2) the running time of GLMVML won’t add too much; (3) GLMVML has a good convergence and ability to process multi-view multi-label data sets; (4) since the model of GLMVML consists of both the global label correlations and local label correlations, so parameter values should be moderate rather than too large or too small.

Multi-View Multi-Label Learning With View-Label-Specific Features

In multi-view multi-label learning, each object is represented by multiple data views, and belongs to multiple class labels simultaneously. Generally, all the data views have a contribution to the multi-label learning task, but their contributions are different. Besides, for each data view, each class label is only associated with a subset data features, and different features have different contributions to each class label. In this paper, we propose a novel framework VLSF for multi-view multi-label learning, i.e., multi-view multi-label learning with View-Label-Specific Features. Specifically, we first learn a low dimensional label-specific data representation for each data view and construct a multi-label classification model based on it by exploiting label correlations and view consensus, and learn the contribution weight of each data view to multi-label learning task for all the class labels jointly. Then, the final prediction can be made by combing the prediction results of all the classifiers and the learned contribution weights. The extensive comparison experiments with the state-of-the-art approaches manifest the effectiveness of the proposed method VLSF.

Latent Semantic Aware Multi-View Multi-Label Classification

For real-world applications, data are often associated with multiple labels and represented with multiple views. Most existing multi-label learning methods do not sufficiently consider the complementary information among multiple views, leading to unsatisfying performance. To address this issue, we propose a novel approach for multi-view multi-label learning based on matrix factorization to exploit complementarity among different views. Specifically, under the assumption that there exists a common representation across different views, the uncovered latent patterns are enforced to be aligned across different views in kernel spaces. In this way, the latent semantic patterns underlying in data could be well uncovered and this enhances the reasonability of the common representation of multiple views. As a result, the consensus multi-view representation is obtained which encodes the complementarity and consistence of different views in latent semantic space. We provide theoretical guarantee for the strict convexity for our method by properly setting parameters. Empirical evidence shows the clear advantages of our method over the state-of-the-art ones.

Block-Row Sparse Multiview Multilabel Learning for Image Classification.

In image analysis, the images are often represented by multiple visual features (also known as multiview features), that aim to better interpret them for achieving remarkable performance of the learning. Since the processes of feature extraction on each view are separated, the multiple visual features of images may include overlap, noise, and redundancy. Thus, learning with all the derived views of the data could decrease the effectiveness. To address this, this paper simultaneously conducts a hierarchical feature selection and a multiview multilabel (MVML) learning for multiview image classification, via embedding a proposed a new block-row regularizer into the MVML framework. The block-row regularizer concatenating a Frobenius norm (F-norm) regularizer and an l(2,1)-norm regularizer is designed to conduct a hierarchical feature selection, in which the F-norm regularizer is used to conduct a high-level feature selection for selecting the informative views (i.e., discarding the uninformative views) and the l(2,1)-norm regularizer is then used to conduct a low-level feature selection on the informative views. The rationale of the use of a block-row regularizer is to avoid the issue of the over-fitting (via the block-row regularizer), to remove redundant views and to preserve the natural group structures of data (via the F-norm regularizer), and to remove noisy features (the l(2,1)-norm regularizer), respectively. We further devise a computationally efficient algorithm to optimize the derived objective function and also theoretically prove the convergence of the proposed optimization method. Finally, the results on real image datasets show that the proposed method outperforms two baseline algorithms and three state-of-the-art algorithms in terms of classification performance.

Multi-view multi-label learning for image annotation

Image annotation is posed as multi-class classification problem. Pursuing higher accuracy is a permanent but not stale challenge in the field of image annotation. To further improve the accuracy of...