Multi-view Semi-supervised Classification Research Articles

Semi-supervised multiview fuzzy broad learning

Semi-supervised learning models often rely on restricted assumptions, and can easily suffer from covariate shift or noise. Few studies have investigated the use of fuzzy rule-based methods in the semi-supervised discipline. To improve model accuracy against covariate shift and to introduce fuzzy methods for interpretability, we first build a semi-supervised fuzzy broad learning model named SSFBLS, which employs a Mean-Teacher framework. Then, a trusted multiview semi-supervised classification method, termed TMSSC, is proposed by integrating the SSFBLS with a multiview fusion network to enhance the robustness of the model. Under the Mean-Teacher framework, SSFBLS involves the Takagi-Sugeno-Kang fuzzy model which can effectively deal with imprecision and uncertainty, and broad learning system which has strong learning ability and high computational efficiency. TMSSC utilizes a trusted mechanism to blend multiple views, so as to enhance its learning ability in semi-supervised scenarios. Experiments on the benchmark datasets demonstrate that the proposed methods have better anti-noise ability, competitive classification accuracy, as well as fast running speed.

Absent Multiview Semisupervised Classification.

With the advent of vast data collection ways, data are often with multiple modalities or coming from multiple sources. Traditional multiview learning often assumes that each example of data appears in all views. However, this assumption is too strict in some real applications such as multisensor surveillance system, where every view suffers from some data absent. In this article, we focus on how to classify such incomplete multiview data in semisupervised scenario and a method called absent multiview semisupervised classification (AMSC) has been proposed. Specifically, partial graph matrices are constructed independently by anchor strategy to measure the relationships among between each pair of present samples on each view. And to obtain unambiguous classification results for all unlabeled data points, AMSC learns view-specific label matrices and a common label matrix simultaneously. AMSC measures the similarity between pair of view-specific label vectors on each view by partial graph matrices, and consider the similarity between view-specific label vectors and class indicator vectors based on the common label matrix. To characterize the contributions of different views, the p th root integration strategy is adopted to incorporate the losses of different views. By further analyzing the relation between the p th root integration strategy and exponential decay integration strategy, we develop an efficient algorithm with proved convergence to solve the proposed nonconvex problem. To validate the effectiveness of AMSC, comparisons are made with some benchmark methods on real-world datasets and in the document classification scenario as well. The experimental results demonstrate the advantages of our proposed approach.

Generative Essential Graph Convolutional Network for Multi-View Semi-Supervised Classification

Generative Essential Graph Convolutional Network for Multi-View Semi-Supervised Classification

Adaptive multi-channel contrastive graph convolutional network with graph and feature fusion

The domain of multi-view semi-supervised classification is an appealing topic in real-world applications. Due to the powerful capability of gathering information from neighbors, Graph Convolutional Network (GCN) has become a hotspot in the classification task. However, most of multi-view classification works based on GCN only assign weights for feature fusion, and directly consider the weighted sum of the adjacency matrices, ignoring the interaction and correlation among features. These may be problematic since aggregating the matrices from less relevant views may destroy the original topology space, leading to undesired performance. To tackle the aforementioned challenges, this paper presents an Adaptive Multi-Channel Graph Convolutional Network (AMC-GCN). To extract the interactive information, AMC-GCN designs a deep interactive feature integration network to incorporate consensus and complementary information. To fuse the graph structures, AMC-GCN exploits the relevance between views and imposes an adjacency matrix fusion network on constructing multiple GCN channels, thereby delivering discriminative information on graphs. To enhance the homogeneity of the framework, AMC-GCN applies a contrastive loss to joint learning during the optimization for classification. With these considerations, AMC-GCN exploits relevant and interactive information between views to promote graph and feature fusion. Substantial experimental results on real-world datasets verify the superiority of AMC-GCN.

Multi-View Graph Convolutional Networks with Differentiable Node Selection

Multi-view data containing complementary and consensus information can facilitate representation learning by exploiting the intact integration of multi-view features. Because most objects in the real world often have underlying connections, organizing multi-view data as heterogeneous graphs is beneficial to extracting latent information among different objects. Due to the powerful capability to gather information of neighborhood nodes, in this article, we apply Graph Convolutional Network (GCN) to cope with heterogeneous graph data originating from multi-view data, which is still under-explored in the field of GCN. In order to improve the quality of network topology and alleviate the interference of noises yielded by graph fusion, some methods undertake sorting operations before the graph convolution procedure. These GCN-based methods generally sort and select the most confident neighborhood nodes for each vertex, such as picking the top- k nodes according to pre-defined confidence values. Nonetheless, this is problematic due to the non-differentiable sorting operators and inflexible graph embedding learning, which may result in blocked gradient computations and undesired performance. To cope with these issues, we propose a joint framework dubbed Multi-view Graph Convolutional Network with Differentiable Node Selection (MGCN-DNS), which is constituted of an adaptive graph fusion layer, a graph learning module, and a differentiable node selection schema. MGCN-DNS accepts multi-channel graph-structural data as inputs and aims to learn more robust graph fusion through a differentiable neural network. The effectiveness of the proposed method is verified by rigorous comparisons with considerable state-of-the-art approaches in terms of multi-view semi-supervised classification tasks, and the experimental results indicate that MGCN-DNS achieves pleasurable performance on several benchmark multi-view datasets.

Affine Subspace Robust Low-Rank Self-Representation: from Matrix to Tensor.

Low-rank self-representation based subspace learning has confirmed its great effectiveness in a broad range of applications. Nevertheless, existing studies mainly focus on exploring the global linear subspace structure, and cannot commendably handle the case where the samples approximately (i.e., the samples contain data errors) lie in several more general affine subspaces. To overcome this drawback, in this paper, we innovatively propose to introduce affine and nonnegative constraints into low-rank self-representation learning. While simple enough, we provide their underlying theoretical insight from a geometric perspective. The union of two constraints geometrically restricts each sample to be expressed as a convex combination of other samples in the same subspace. In this way, when exploring the global affine subspace structure, we can also consider the specific local distribution of data in each subspace. To comprehensively demonstrate the benefits of introducing two constraints, we instantiate three low-rank self-representation methods ranging from single-view low-rank matrix learning to multi-view low-rank tensor learning. We carefully design the solution algorithms to efficiently optimize the proposed three approaches. Extensive experiments are conducted on three typical tasks, including single-view subspace clustering, multi-view subspace clustering, and multi-view semi-supervised classification. The notably superior experimental results powerfully verify the effectiveness of our proposals.

Adaptive collaborative fusion for multi-view semi-supervised classification

Multi-view semi-supervised classification is inherently a challenging task in multi-view learning due to the lack of label information. Existing methods generally suffer from insufficient data fusion, expensive computation cost in the solution procedure and fail in tackling unseen samples directly, intensively limiting their applicability and efficiency in real scenarios. To address these issues, we propose an adaptive collaborative fusion method, seeking for an appropriate representation and fusion for multi-view data. The main advantage of the proposed method is that it simultaneously fuses both multiple feature projections and similarity graphs to learn a joint projection subspace as well as a unified similarity graph that fully preserve the correlation and distinction among views. Meanwhile, our method can coalesce different views in an adaptive-weighting manner, making the learned subspace more discriminative and facilitating label propagation on the fused graph. Furthermore, an acceleration strategy has been designed to reduce the computational complexity, thereby making the proposed method scalable to relatively large-scale data. Finally, an alternating optimization has been adopted to solve the formulated objective function. Extensive experiments on synthetic and real-world datasets are conducted to demonstrate the effectiveness and superiority of our proposed method.

Learnable graph convolutional network and feature fusion for multi-view learning

In practical applications, multi-view data depicting objects from assorted perspectives can facilitate the accuracy increase of learning algorithms. However, given multi-view data, there is limited work for learning discriminative node relationships and graph information simultaneously via graph convolutional network that has drawn the attention from considerable researchers in recent years. Most of existing methods only consider the weighted sum of adjacency matrices, yet a joint neural network of both feature and graph fusion is still under-explored. To cope with these issues, this paper proposes a joint deep learning framework called Learnable Graph Convolutional Network and Feature Fusion (LGCN-FF), consisting of two modules: feature fusion network and learnable graph convolutional network. The former aims to learn an underlying feature representation from heterogeneous views, while the latter explores a more discriminative graph fusion via learnable weights and a parametric activation function dubbed Differentiable Shrinkage Activation (DSA) function. The proposed LGCN-FF is validated to be superior to various state-of-the-art methods in multi-view semi-supervised classification.

MGLNN: Semi-supervised learning via Multiple Graph Cooperative Learning Neural Networks

In many machine learning applications, data are coming with multiple graphs, which is known as the multiple graph learning problem. The problem of multiple graph learning is to learn consistent representation by exploiting the complementary information of multiple graphs. Graph Learning Neural Networks (GLNNs) have been demonstrated powerfully for graph data representation and semi-supervised classification tasks. However, Existing GLNNs are mainly developed for single graph data which cannot be utilized for multiple graph data representation. In this paper, we propose a novel learning framework, called Multiple Graph Learning Neural Networks (MGLNN), for multiple graph learning and multi-view semi-supervised classification. The goal of MGLNN is to learn an optimal graph structure from multiple graph structures that best serves GNNs’ learning which integrates multiple graph learning and GNNs’ representation simultaneously. The proposed MGLNN is a general framework which can incorporate any specific GNN model to deal with multiple graphs. A general algorithm has also been developed to optimize/train the proposed MGLNN model. Experimental results on several datasets demonstrate that MGLNN outperforms some other related methods on semi-supervised classification tasks.

Semi-supervised learning based on intra-view heterogeneity and inter-view compatibility for image classification

To achieve high value of classification results by using a limited number of training samples, designing a multi-view semi-supervised classification model is extremely urgent. Moreover, considering that graph learning can fully capture the complex data structure information, we propose a graph-based multi-view semi-supervised classification approach named as Semi-supervised Classification based on Intra-view Heterogeneity and Inter-view Compatibility (SSC_IHIC). Specifically, the similarity between two samples can be measured based on the Euclidean distance. Considering that heterogeneity exists in intra-view sub-features, hence, the weights are learned adaptively. Besides, based on the inter-view compatibility, the consensus similarity graph is constructed on which the labels are propagated. To verify the effectiveness, the proposed approach is conducted on four data sets including MSRC-v1, Handwritten (HW), Cal101-7 and Cal101-20. From the experimental results, we can see that the classification performance about proposed method outperforms the single view classification methods and the state-of-the-art multi-view classification methods.

GRNet: Graph-based remodeling network for multi-view semi-supervised classification

Multi-view semi-supervised classification (MSSC) focuses on exploring information from multiple views of labeled and unlabeled data to boost classification performance. However, most of the existing methods build models for each view individually, therefore, the potential relationship between different views can’t be fully explored. Additionally, they either focus on the correlations for consistency or maximize the independence for complementarity. Consistency and complementarity are equally important for multi-view learning. Therefore, this work proposes a novel Graph-based Remodeling Network for MSSC (GRNet), which can explore the potential relationship of multiple views and balance the consistency and complementation adaptively. Specifically, this model integrates multiple views and then generates reformed pseudo views by an attention-based ensemble learning strategy. Moreover, to exploit the information of unlabeled data, this work introduces graph regularization for such a goal. Extensive experiments on several datasets demonstrate the effectiveness and efficiency of the proposed method.

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.

Embedding Regularizer Learning for Multi-View Semi-Supervised Classification.

Classification remains challenging when confronted with the existence of multi-view data with limited labels. In this paper, we propose an embedding regularizer learning scheme for multi-view semi-supervised classification (ERL-MVSC). The proposed framework integrates diversity, sparsity and consensus to dexterously manipulate multi-view data with limited labels. To encourage diversity, ERL-MVSC recasts a linear regression model to derive view-specific embedding regularizers and automatically determines their weights. This is able to tactfully incorporate complementary information of different views. To ensure sparsity, ERL-MVSC imposes l2,1 -norm on a fused embedding regularizer to exploit the sparse local structure of samples, thereby conveying valuable classification information and enhancing the robustness against noise/outliers. To enhance consensus, ERL-MVSC learns a shared predicted label matrix, which serves as the comment target of multi-view classification. With these techniques, we formulate ERL-MVSC as a joint optimization problem of an embedding regularizer and a predicted label matrix, which can be solved by a coordinate descent method. Extensive experimental results on real-world datasets demonstrate the effectiveness and superiority of the proposed algorithm.

Latent multi-view semi-supervised classification by using graph learning

Multi-view learning is a hot research direction in the field of machine learning and pattern recognition, which is attracting more and more attention recently. In the real world, the available data commonly include a small number of labeled samples and a large number of unlabeled samples. In this paper, we propose a latent multi-view semi-supervised classification method by using graph learning. This work recovers a latent intact representation to utilize the complementary information of the multi-view data. In addition, an adaptive graph learning technique is adopted to explore the local structure of this latent intact representation. To fully use this latent intact representation to discover the label information of the unlabeled data, we consider to unify the procedures of computing the latent intact representation and the labels of unlabeled data as a whole. An alternating optimization algorithm is designed to effectively solve the optimization of the proposed method. Extensive experimental results demonstrate the effectiveness of our proposed method.

Joint consensus and diversity for multi-view semi-supervised classification

As data can be acquired in an ever-increasing number of ways, multi-view data is becoming more and more available. Considering the high price of labeling data in many machine learning applications, we focus on multi-view semi-supervised classification problem. To address this problem, in this paper, we propose a method called joint consensus and diversity for multi-view semi-supervised classification, which learns a common label matrix for all training samples and view-specific classifiers simultaneously. A novel classification loss named probabilistic square hinge loss is proposed, which avoids the incorrect penalization problem and characterizes the contribution of training samples according to its uncertainty. Power mean is introduced to incorporate the losses of different views, which contains the auto-weighted strategy as a special case and distinguishes the importance of various views. To solve the non-convex minimization problem, we prove that its solution can be obtained from another problem with introduced variables. And an efficient algorithm with proved convergence is developed for optimization. Extensive experimental results on nine datasets demonstrate the effectiveness of the proposed algorithm.

Deep Constrained Low-Rank Subspace Learning for Multi-View Semi-Supervised Classification

Semi-supervised classification receives increasing interests because it can predict class labels based on both limited labeled and sufficient unlabeled data. In this letter, we propose a deep constrained low-rank subspace learning (DCLSL) method for multi-view semi-supervised classification. Specifically, we integrate deep constrained matrix factorization, low-rank subspace learning, and class label learning into a unified objective function to jointly learn data similarity matrices and class label matrix. DCLSL is able to obtain the discriminative subspace representation of each view and effectively aggregate similarity matrices of multiple views, resulting in better classification performance. Experimental results on various datasets demonstrate the effectiveness of our method.

Adaptive Semi-Supervised Classification by Joint Global and Local Graph

Recently, semisupervised classification methods have been widely applied in many scientific fields, among them, the graph-based methods of semisupervised classification have attracted great attention due to their effectiveness. However, most existing graph-based multi-view methods only construct a graph on each view, and cannot make full use of the abundant information provided by multi-view data. In addition, some semisupervised classification methods directly learn a fixed graph with original input data which contain noise that result in unreliable graphs. Based on these considerations, we propose a novel adaptive multi-view semisupervised classification model which construct a joint global-local graph with total views to attain the connection between each view and within each view. Our model can obtain a better consistency structure and learn the weight coefficient automatically after finite iterations. We also develop a nongreedy optimization algorithm to solve our objective function. In our model, we studied the effect of the global structure on the overall classification performance, and the experiments verified the theoretical derivation. We also studied the effect of label ratio on classification accuracy in different algorithms. The experimental results show that the classification accuracy of our algorithm is higher than other existing algorithms in the range of moderate label ratio so that it has great advantages in practical applications. When the global graph is added, the model still has good performance in terms of running time and convergence rate. The comprehensive experiments on different real-world datasets show the effectiveness of the proposed approach and demonstrate the advantage over other methods.

Hyper-Laplacian Regularized Multilinear Multiview Self-Representations for Clustering and Semisupervised Learning.

In this paper, we address the multiview nonlinear subspace representation problem. Traditional multiview subspace learning methods assume that the heterogeneous features of the data usually lie within the union of multiple linear subspaces. However, instead of linear subspaces, the data feature actually resides in multiple nonlinear subspaces in many real-world applications, resulting in unsatisfactory clustering performance. To overcome this, we propose a hyper-Laplacian regularized multilinear multiview self-representation model, which is referred to as HLR-M2VS, to jointly learn multiple views correlation and a local geometrical structure in a unified tensor space and view-specific self-representation feature spaces, respectively. In unified tensor space, a well-founded tensor low-rank regularization is adopted to impose on the self-representation coefficient tensor to ensure global consensus among different views. In view-specific feature space, hypergraph-induced hyper-Laplacian regularization is utilized to preserve the local geometrical structure embedded in a high-dimensional ambient space. An efficient algorithm is then derived to solve the optimization problem of the established model with theoretical convergence guarantee. Furthermore, the proposed model can be extended to semisupervised classification without introducing any additional parameters. An extensive experiment of our method is conducted on many challenging datasets, where a clear advance over state-of-the-art multiview clustering and multiview semisupervised classification approaches is achieved.

Convex Multiview Semi-Supervised Classification.

In many practical applications, there are a great number of unlabeled samples available, while labeling them is a costly and tedious process. Therefore, how to utilize unlabeled samples to assist digging out potential information about the problem is very important. In this paper, we study a multiclass semi-supervised classification task in the context of multiview data. First, an optimization method named Parametric multiview semi-supervised classification (PMSSC) is proposed, where the built classifier for each individual view is explicitly combined with a weight factor. By analyzing the weakness of it, a new adapted weight learning strategy is further formulated, and we come to the convex multiview semi-supervised classification (CMSSC) method. Comparing with the PMSSC, this method has two significant properties. First, without too much loss in performance, the newly used weight learning technique achieves eliminating a hyperparameter, and thus it becomes more compact in form and practical to use. Second, as its name implies, the CMSSC models a convex problem, which avoids the local-minimum problem. Experimental results on several multiview data sets demonstrate that the proposed methods achieve better performances than recent representative methods and the CMSSC is preferred due to its good traits.