Despite the impressive clustering performance and efficiency in characterizing both the relationship between the data and cluster structure, most existing graph-based multi-view clustering methods still have the following drawbacks. They suffer from the expensive time burden due to both the construction of graphs and eigen-decomposition of Laplacian matrix. Moreover, none of them simultaneously considers the similarity of inter-view and similarity of intra-view. In this article, we propose a variance-based de-correlation anchor selection strategy for bipartite construction. The selected anchors not only cover the whole classes but also characterize the intrinsic structure of data. Following that, we present a tensorized bipartite graph learning for multi-view clustering (TBGL). Specifically, TBGL exploits the similarity of inter-view by minimizing the tensor Schatten p-norm, which well exploits both the spatial structure and complementary information embedded in the bipartite graphs of views. We exploit the similarity of intra-view by using the $\ell _{\textrm {1,2}}$-norm minimization regularization and connectivity constraint on each bipartite graph. So the learned graph not only well encodes discriminative information but also has the exact connected components which directly indicates the clusters of data. Moreover, we solve TBGL by an efficient algorithm which is time-economical and has good convergence. Extensive experimental results demonstrate that TBGL is superior to the state-of-the-art methods. Codes and datasets are available: https://github.com/xdweixia/TBGL-MVC.

Deep multi-view subspace clustering has achieved promising performance compared with other multi-view clustering. However, existing deep multi-view subspace clustering only considers the global structure for all views, and they ignore the local geometric structure among each view. In addition, they cannot learn discriminative feature on different clusters of different views, i.e., inter-cluster difference. To solve these problems, in this paper, we propose a novel Deep Multi-view Subspace Clustering with Unified and Discriminative Learning (DMSC-UDL). DMSC-UDL combines global and local structures with self-expression layer. The global and local structures help each other forward and achieve small distance between samples of the same cluster. To make samples in different clusters of different views farther, DMSC-UDL uses a discriminative constraint between different views. In this way, DMSC-UDL makes the same cluster's samples have large weights, while different clusters' samples have small weights. Thus, it can learn a better shared connection matrix for multi-view clustering. Extensive experimental results reveal that the proposed multi-view clustering method is superior to several state-of-the-art multi-view clustering methods in terms of performance.

Nowadays, with the rapid development of data collection sources and feature extraction methods, multi-view data are getting easy to obtain and have received increasing research attention in recent years, among which, multi-view clustering (MVC) forms a mainstream research direction and is widely used in data analysis. However, existing MVC methods mainly assume that each sample appears in all the views, without considering the incomplete view case due to data corruption, sensor failure, equipment malfunction, etc. In this study, we design and build a generative partial multi-view clustering model with adaptive fusion and cycle consistency, named as GP-MVC, to solve the incomplete multi-view problem by explicitly generating the data of missing views. The main idea of GP-MVC lies in two-fold. First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the shared cluster structure across multiple views. Second, view-specific generative adversarial networks with multi-view cycle consistency are developed to generate the missing data of one view conditioning on the shared representation given by other views. These two steps could be promoted mutually, where the learned common representation facilitates data imputation and the generated data could further explores the view consistency. Moreover, an weighted adaptive fusion scheme is implemented to exploit the complementary information among different views. Experimental results on four benchmark datasets are provided to show the effectiveness of the proposed GP-MVC over the state-of-the-art methods.

Despite the promising preliminary results, existing graph convolutional network (GCN) based multi-view learning methods directly use the graph structure as view descriptor, which may inhibit the ability of multi-view learning for multimedia data. The major reason is that, in real multimedia applications, the graph structure may contain outliers. Moreover, they fail to take advantage of the information embedded in the inaccurate clustering labels obtained from their proposed methods, resulting in inferior clustering results. These observations motivate us to study whether there is a better alternative GCN based framework for multi-view clustering. To this end, in this paper, we propose an end-to-end self-supervised graph convolutional network for multi-view clustering (SGCMC). Specifically, SGCMC constructs a new view descriptor for graph-structured data by mapping the raw node content into the complex space via Euler transformation, which not only suppresses outliers but also reveals non-linear patterns embedded in data. Meanwhile, the proposed SGCMC uses the clustering labels to guide the learning of the latent representation and coefficient matrix, and the latter in turn is used to conduct the subsequent node clustering. By this way, clustering and representation learning are seamlessly connected, with the aim to achieve better clustering results. Extensive experimental results indicate that the proposed SGCMC outperforms the state-of-the-art methods.

Incomplete multiview clustering is a challenging problem in the domain of unsupervised learning. However, the existing incomplete multiview clustering methods only consider the similarity structure of intraview while neglecting the similarity structure of interview. Thus, they cannot take advantage of both the complementary information and spatial structure embedded in similarity matrices of different views. To this end, we complete the incomplete graph with missing data referring to tensor complete and present a novel and effective model to handel the incomplete multiview clustering task. To be specific, we consider the similarity of the interview graphs via the tensor Schatten $p$ -norm-based completion technique to make use of both the complementary information and spatial structure. Meanwhile, we employ the connectivity constraint for similarity matrices of different views such that the connected components approximately represent clusters. Thus, the learned entire graph not only has the low-rank structure but also well characterizes the relationship between unmissing data. Extensive experiments show the promising performance of the proposed method comparing with several incomplete multiview approaches in the clustering tasks.

Cross-modal clustering aims to cluster the high-similar cross-modal data into one group while separating the dissimilar data. Despite the promising cross-modal methods have developed in recent years, existing state-of-the-arts cannot effectively capture the correlations between cross-modal data when encountering with incomplete cross-modal data, which can gravely degrade the clustering performance. To well tackle the above scenario, we propose a novel incomplete cross-modal clustering method that integrates canonical correlation analysis and exclusive representation, named incomplete Cross-modal Subspace Clustering (i.e., iCmSC). To learn a consistent subspace representation among incomplete cross-modal data, we maximize the intrinsic correlations among different modalities by deep canonical correlation analysis (DCCA), while an exclusive self-expression layer is proposed after the output layers of DCCA. We exploit a ℓ _1,2 -norm regularization in the learned subspace to make the learned representation more discriminative, which makes samples between different clusters mutually exclusive and samples among the same cluster attractive to each other. Meanwhile, the decoding networks are employed to reconstruct the feature representation, and further preserve the structural information among the original cross-modal data. To the end, we demonstrate the effectiveness of the proposed iCmSC via extensive experiments, which can justify that iCmSC achieves consistently large improvement compared with the state-of-the-arts.