Multi-view Semi-supervised Learning Research Articles

Multi-view semi-supervised learning with adaptive graph fusion

Multi-view Semi-supervised Learning (MSL) is effective in using limited labels and considerable label-free data to improve learning performance. It has been successfully applied to a lot of real scenarios. In this study, we propose a model, termed MSL with Adaptive Graph Fusion (MSLAGF), which provides a novel solution for MSL. Unlike most existing methods propagating label information through the linear combination of pre-built fixed view-based similarity graphs, MSLAGF merges view-based graph construction, graph fusion, and label propagation. It adaptively learns view-specific graphs and automatically assigns weight coefficients to them. A multi-view fusion optimal graph is cleverly learned depending not only on the raw feature space but also on the dynamically predicted label space. Moreover, we present an efficient optimization algorithm to solve the formulated model. The view-specific graphs, the weight coefficients, the optimal graph, and the predicted labels are mutually negotiated and optimized in the optimization procedure. Extensive experimental results on six benchmark datasets validate the superiority.

Robust semi-supervised multi-view graph learning with sharable and individual structure

The construction of a high-quality multi-view consensus graph is key to graph-based semi-supervised multi-view learning (GSSMvL) methods. However, most existing GSSMvL methods explore sample relationships in the original multi-view feature space, which obtains a contaminated graph that cannot reveal the underlying manifold structure of the samples. Moreover, traditional GSSMvL methods fail to explore the diverse structures of multi-view features, which may lose their complementary information and lead to a suboptimal graph. In this paper, we propose a novel unified robust semi-supervised multi-view graph learning framework based on the sharable and individual structure (RSSMvSI), which can eliminate the influence of noise and exploit the knowledge of multi-view data in a reasonable manner. Specifically, we first learn clean data by manipulating sparse noise with l2,1 norm. We then simultaneously explore the sharable and individual self-representation subspace on the learned clean multi-view data. The key point is that noisy data does not participate in subspace learning, which improves the robustness of the proposed method. By constructing the optimal consensus graph with the learned sharable and individual subspace, RSSMvSI can better utilize the complementary information of multi-view data and approximate the manifold structure of samples. To the best of our knowledge, this is the first attempt to learn the self-representation subspace on recovered multi-view clean data. Extensive experiments on various real-world multi-view datasets demonstrate the superiority and robustness against state-of-the-art methods.

Inductive Multi-View Semi-supervised Learning with a Consensus Graph

Inductive Multi-View Semi-supervised Learning with a Consensus Graph

Interpretable Graph Convolutional Network for Multi-View Semi-Supervised Learning

As real-world data become increasingly heterogeneous, multi-view semi-supervised learning has garnered widespread attention. Although existing studies have made efforts towards this and achieved decent performance, they are restricted to shallow models and how to mine deeper information from multiple views remains to be investigated. As a recently emerged neural network, Graph Convolutional Network (GCN) exploits graph structure to propagate label signals and has achieved encouraging performance, and it has been widely employed in various fields. Nonetheless, research on solving multi-view learning problems via GCN is limited and lacks interpretability. To address this gap, in this paper we propose a framework termed Interpretable Multi-view Graph Convolutional Network (IMvGCN <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>¹</label> Code is available at <uri>https://github.com/ZhihaoWu99/IMvGCN</uri>. </fn> ). We first combine the reconstruction error and Laplacian embedding to formulate a multi-view learning problem that explores the original space from feature and topology perspectives. In light of a series of derivations, we establish a potential connection between GCN and multi-view learning, which holds significance for both domains. Furthermore, we propose an orthogonal normalization method to guarantee the mathematical connection, which solves the intractable problem of orthogonal constraints in deep learning. In addition, the proposed framework is applied to the multi-view semi-supervised learning task. Comprehensive experiments demonstrate the superiority of our proposed method over other state-of-the-art methods.

MMatch: Semi-Supervised Discriminative Representation Learning for Multi-View Classification

Semi-supervised multi-view learning has been an important research topic due to its capability to exploit complementary information from unlabeled multi-view data. This work proposes MMatch, a new semi-supervised discriminative representation learning method for multi-view classification. Unlike existing multi-view representation learning methods that seldom consider the negative impact caused by particular views with unclear classification structures (weak discriminative views). MMatch jointly learns view-specific representations and class probabilities of training data. The representations concatenated to integrate multiple views’ information to form a global representation. Moreover, MMatch performs the smoothness constraint on the class probabilities of the global representation to improve pseudo labels, whereas the pseudo labels regularize the structure of view-specific representations. A discriminative global representation is mined with the training process, and the negative impact of weak discriminative views is overcome. Besides, MMatch learns consistent classification while preserving diverse information from multiple views. Experiments on several multi-view datasets demonstrate the effectiveness of MMatch.

Co-consensus semi-supervised multi-view learning with orthogonal non-negative matrix factorization

Semi-supervised multi-view learning has recently achieved appealing performance with the consensus relation between samples. However, in addition to the relation between samples, the relation between samples and their assemble centroid is also important to the learning. In this paper, we propose a novel model based on orthogonal non-negative matrix factorization, which allows exploring both the consensus relations between samples and between samples and their assemble centroid. Since this model utilizes more consensus information to guide the multi-view learning, it can lead to better performance. Meanwhile, we theoretically derive a proposition about the equivalency between the partial orthogonality and the full orthogonality. Based on this proposition, the orthogonality constraint and the label constraint are simultaneously implemented in the proposed model. Experimental evaluations on five real-world datasets show that our approach outperforms the state-of-the-art methods, where the improvement is 6% average in terms of ARI index.

Semi-supervised multi-view binary learning for large-scale image clustering

Semi-supervised multi-view binary learning for large-scale image clustering

Semi-supervised multi-view graph convolutional networks with application to webpage classification

Semi-supervised multi-view learning (SML) is a hot research topic in recent years, with webpage classification being a typical application domain. The performance of SML is further boosted by the successful introduction of graph convolutional network (GCN) for learning discriminant node representations. However, there remains much space to improve the GCN-based SML technique, particularly on how to adaptively learn optimal graph structures for multi-view graph convolutional representation learning and make full use of the label and structure information in labeled and unlabeled multi-view samples. In this paper, we propose a novel SML approach named semi-supervised multi-view graph convolutional networks (SMGCN) for webpage classification. It contains a multi-view graph construction module and a semi-supervised multi-view graph convolutional representation learning module, which are integrated into a unified network architecture. The former aims to obtain optimal graph structure for each view. And the latter performs graph convolutional representation learning for each view, and provides an inter-view attention scheme to fuse multi-view representations. Network training is guided by the losses defined on both label and feature spaces, such that the label and structure information in labeled and unlabeled data is fully explored. Experiments on two widely used webpage datasets demonstrate that SMGCN can achieve state-of-the-art classification performance.

Co-embedding: a semi-supervised multi-view representation learning approach

Co-embedding: a semi-supervised multi-view representation learning approach

Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes

Semisupervised learning is an idea that addresses how to use a large number of unlabeled samples and a limited number of labeled samples to learn decision knowledge together. In this paper, we propose a multitask multiview semisupervised learning model based on partial differential equation random field and Hilbert independent standard probability image genus attribute model, i.e., shared semantics. In the framework of the image-like genus attribute model, data from different data sources are generated by their shared hidden space representation. Different from the traditional model, this paper uses the Hilbert independence criterion to inscribe the shared relationship of hidden expressions. Meanwhile, to exploit the correlations between labels in the label space as well, this paper uses the partial differential equation random field to inscribe the correlations between different kinds of labels in the label space and the correlations between hidden features and labels. Using the variational expectation-maximization algorithm, the whole generative process model can be inferred. To verify the effectiveness of the model, two artificial datasets and three real datasets are tested in this paper, and the experimental results verify the effectiveness of the algorithm in the paper. On the one hand, it not only improves the classification accuracy of the multiclassification problem and the multilabel problem; it also outputs the association structure between different kinds of labels and between hidden features and labels.

Semi-supervised multi-view learning by using label propagation based non-negative matrix factorization

Semi-supervised multi-view learning methods aim to boost the learning performance by conjunction with labeled data, because the label information can enhance the discriminant ability of the learned model. Recently, non-negative matrix factorization has received widespread attention in semi-supervised multi-view learning due to its powerful ability of feature extraction. However, the problem of very limited available labeled data is seldom considered. In this case, it is hard for existing approaches to obtain satisfied performance. Motivated by that the label propagation can classify a large number of unlabeled data with few labeled data, in this paper, we propose a novel semi-supervised multi-view learning approach to address the problem of sparse labeled data, called Label Propagation based Non-negative Matrix Factorization (LPNMF). In our model, the intrinsic manifold structure of data is constructed by the adaptive neighbors technology. Based on this intrinsic manifold structure, the label propagation is further employed to make full use of the limited labeled data. Besides, we design an efficient alternating algorithm for solving the optimization problem and provide theoretical analysis on its convergence properties and computational complexity. Finally, experiments on four real-world datasets demonstrate the advantage of our proposed methods, with comparison to the state-of-the-art methods.

Human action recognition method based on multi-view semi-supervised ensemble learning

Human action recognition method based on multi-view semi-supervised ensemble learning

Flexible multi-view semi-supervised learning with unified graph

At present, the diversity of data acquisition boosts the growth of multi-view data and the lack of label information. Since manually labeling is expensive and impractical, it is practical to enhance learning performance with a small amount of labeled data and a large amount of unlabeled data. In this study, we propose a novel multi-view semi-supervised learning (MSEL) framework termed flexible MSEL (FMSEL) with unified graph. In this framework, two flexible regression residual terms are introduced. One is a linear penalty term, which adaptively weighs the diverse contributions of different views and properly learns a well structured unified graph. The other is a relaxation regularization term, which finds the optimal prediction and the linear regression function. Both the prediction of samples in the database and new-coming data are supported. Moreover, during the process, the unified graph learns depending on the data structure and dynamically updated label information. Further, we provide an alternating optimization algorithm to iteratively solve the resultant objective problem and theoretically analyze the corresponding complexities. Extensive experiments conducted on synthetic and public datasets demonstrate the superiority of FMSEL.

Seeded random walk for multi-view semi-supervised classification

Recently, multi-view learning has captured widespread attention in the machine learning area, yet it is still crucial and challenging to exploit beneficial patterns from multi-view data. Specifically, very limited work has been devoted to multi-view semi-supervised learning, where only a small number of labeled data points are available for model training. Therefore, a simple yet efficient seeded random walk scheme is proposed in this paper to address the multi-view semi-supervised classification problem, where known labeled data points serve as random seeds to be walked with certain probability. In this scheme, the semi-supervised classification indicator is obtained based primarily on an arrival probability and a reward matrix, which are computed by leveraging an initial distribution from some random seeds. Besides, theoretical analyses are then provided to indicate a connection of the proposed model with the existing manifold ranking method. Finally, comprehensive experiments on eight publicly available data sets demonstrate the superiority of the proposed model against compared state-of-the-art semi-supervised methods and fully supervised classifiers. Furthermore, experimental results also suggest that the proposed method comes with positive robustness and promising generalization capability in terms of data classification.

Accelerated manifold embedding for multi-view semi-supervised classification

Multi-view semi-supervised learning has gained much attention since a great number of unlabeled multi-view data are easy to obtain while few labeled data are available. Accordingly, how to utilize the relationship between labeled and unlabeled data is of significance in multi-view semi-supervised learning. In this paper, we propose an auto-weighted manifold embedding model to address multi-view semi-supervised classification problems, where only a small percentage of labeled data points are used for model training. In the proposed model, data points close to each other in the feature space will be assigned to similar classes in the label space through manifold embedding. Accordingly, the class information of labeled data will be employed in the prediction process for unlabeled data. Moreover, an optimal weight for each view of multi-view data is learned automatically, which enables an encouraging fusion quality for multi-view manifold embedding. To further speed up the calculation process and reduce the computational complexity of the proposed model, an effective accelerated auto-weighted manifold embedding scheme is developed. Besides, theoretical analyses are then provided to indicate a tight approximation bound for the primary manifold embedding method, while the accelerated method is designed by an order decrease of magnitude of computational complexity. Finally, comprehensive experiments on eight publicly available data sets demonstrate the superiority of the proposed models over compared state-of-the-art semi-supervised methods and fully supervised classifiers. Furthermore, the experimental results are suggestive of positive robustness and promising generalization capacity of the proposed methods.

Multiview Semi-Supervised Learning Model for Image Classification

Semi-supervised learning models for multiview data are important in image classification tasks, since heterogeneous features are easy to obtain and semi-supervised schemes are economical and effective. To model the view importance, conventional graph-based multiview learning models learn a linear combination of views while assuming a priori weights distribution. In this paper, we present a novel structural regularized semi-supervised model for multiview data, termed Adaptive MUltiview SEmi-supervised model (AMUSE). Our new model learns weights from a priori graph structure, which is more reasonable than weight regularization. Theoretical analysis reveals the significant difference between AMUSE and the prior arts. An efficient optimization algorithm is provided to solve the new model. Experimental results on six real-world data sets demonstrate the effectiveness of the structural regularized weights learning scheme.

Joint auto-weighted graph fusion and scalable semi-supervised learning

Graph carries out a key role in graph-based semi-supervised label propagation, as it clarifies the structure of the data manifold. The performance of label propagation methods depends on the adopted graph and can be enhanced by merging different graphs that are obtained from multiple sources of information. While there exist algorithms that perform graph fusion they have several weaknesses. Most of these algorithms define graph fusion and label propagation as two separate tasks. Moreover, when the number of data expands, these strategies are not well-suited due to the use of transductive learning in the label propagation phase which makes the label prediction for unseen samples difficult. Furthermore, very few algorithms extract the information contained in the label space. Additionally, most of the graph fusion techniques adopt equal or static weights for different views, which is not the best choice as distinctive features (hence different graphs) contain various information. To overcome these shortcomings, we propose an Auto-weighted Multi-view Semi-Supervised Learning method (AMSSL), which is based on an inductive learning algorithm (i.e., Flexible Manifold Embedding) and profited a projection matrix for predicting the labels of out-of-sample data. The proposed AMSSL method represents a unified framework that dynamically fuses various information obtained from different features and also from the label space and adaptively designates appropriate weights according to the usefulness of each view. Our experimental results on seven small and large image datasets demonstrate the superiority of the proposed method compared to the use of one single feature and other state-of-the-art graph fusion methods.

Multiple-feature-driven co-training method for crop mapping based on remote sensing time series imagery

ABSTRACT Remote sensing time series imagery (RSTSI) provides a useful tool for crop mapping, as it provides crucial spectral, temporal, and spatial (STS) features. However, its high dimensionality coupled with the limited number of training samples leads to an ill-posed classification problem and the Hughes phenomenon. To solve this problem, this study presents a multiple-feature-driven co-training method (MFDC) for accurately mapping crop types based on RSTSI with a limited number of training samples. In MFDC, four complementary pre-defined views, which represent STS features, are generated for the utilization of multiple features. Then, to enhance the classifier’s generalization ability, a novel labelled sample augmentation method that combines the Breaking Tiles algorithm and co-training is proposed. Third, to ensure the effectiveness of ensemble learning in co-training as well as to further speed up the learning process, a multi-view semi-supervised feature learning algorithm that expands the single view semi-supervised learning algorithm to multiple views is proposed and embedded in co-training. Finally, a weighted majority vote method is utilized to obtain the classification results. The experimental results for study areas in the United States indicate that the proposed method can accurately map crop types with a limited number of labelled training samples without a significant computational cost.

General multi-view semi-supervised least squares support vector machines with multi-manifold regularization

Multi-view semi-supervised learning achieves great success in recent years which leverages information among labeled and unlabeled multi-view data to improve the generalization performance. So far two classical two-view semi-supervised learning methods are multi-view Laplacian support vector machines (MvLapSVM) and multi-view Laplacian twin support vector machines (MvLapTSVM). But they can only deal with two-view classification problems and cannot deal with general multi-view classification problems. They both solve the quadratic programming problems (QPPs) so that their time complexity is quite high. In this paper, we formulate general multi-view Laplacian least squares support vector machines (GMvLapSVM) and general multi-view Laplacian least squares twin support vector machines (GMvLapTSVM) which solve linear equations as compared to QPPs in MvLapSVM and MvLapTSVM. They can handle the general multi-view classification problems by combining multiple different views in a non-pairwise way. The disagreement among different views is considered as a regularization term in the objective function to explore the consensus information. Multi-manifold regularization is adopted for multi-view semi-supervised learning. Combination weights for all view in the norm regularization terms are adopted to exploit complementarity information among distinct views. Finally, an efficient alternating algorithm is proposed for optimization. Experiments are performed on various real world datasets, which give state-of-the-art generalization performance.

Co-GCN for Multi-View Semi-Supervised Learning

In many real-world applications, the data have several disjoint sets of features and each set is called as a view. Researchers have developed many multi-view learning methods in the past decade. In this paper, we bring Graph Convolutional Network (GCN) into multi-view learning and propose a novel multi-view semi-supervised learning method Co-GCN by adaptively exploiting the graph information from the multiple views with combined Laplacians. Experimental results on real-world data sets verify that Co-GCN can achieve better performance compared with state-of-the-art multi-view semi-supervised methods.