Incomplete multiview clustering (IMVC) has received extensive attention in recent years. However, existing works still have several shortcomings: 1) some works ignore the correlation of sample pairs in the global structural distribution; 2) many methods are computational expensive, thus cannot be applicable to the large-scale incomplete data clustering tasks; and 3) some methods ignore the refinement of the bipartite graph structure. To address the above issues, we propose a novel anchor graph network for IMVC, which includes a generative model and a similarity metric network. Concretely, the method uses a generative model to construct bipartite graphs, which can mine latent global structure distributions of sample pairs. Later, we use graph convolution network (GCN) with the constructed bipartite graphs to learn the structural embeddings. Notably, the introduction of bipartite graphs can greatly reduce the computational complexity and thus enable our model to handle large-scale data. Unlike previous works based on bipartite graph, our method employs bipartite graphs to guide the learning process in GCNs. In addition, an innovative adaptive learning strategy that can construct robust bipartite graphs is incorporated into our method. Extensive experiments demonstrate that our method achieves the comparable or superior performance compared with the state-of-the-art methods.

In order to reduce the negative effect of missing data on clustering, incomplete multiview clustering (IMVC) has become an important research content in machine learning. At present, graph-based methods are widely used in IMVC, but these methods still have some defects. First, some of the methods overlook potential relationships across views. Second, most of the methods depend on local structure information and ignore the global structure information. Third, most of the methods cannot use both global structure information and potential information across views to adaptively recover the incomplete relationship structure. To address the above issues, we propose a unified optimization framework to learn reasonable affinity relationships, called low-rank graph completion-based IMVC (LRGR_IMVC). 1) Our method introduces adaptive graph embedding to effectively explore the potential relationship among views; 2) we append a low-rank constraint to adequately exploit the global structure information among views; and 3) this method unites related information within views, potential information across views, and global structure information to adaptively recover the incomplete graph structure and obtain complete affinity relationships. Experimental results on several commonly used datasets show that the proposed method achieves better clustering performance significantly than some of the most advanced methods.

Advanced deep multi-view subspace clustering methods are based on the self-expressive model, which has achieved impressive performance. However, most existing works have several limitations: 1) They endure high computational complexity when learning a consistent affinity matrix, impeding their capacity to handle large-scale multi-view data; 2) The global and local structure information of multi-view data remains under-explored. To tackle these challenges, we propose a simplistic but comprehensive framework called Multi-view Self-Expressive Subspace Clustering (MSESC) network. Specifically, we design a deep metric network to replace the conventional self-expressive model, which can directly and efficiently produce the intrinsic similarity values of any instance-pairs of all views. Moreover, our method explores global and local structure information from the connectivity of instance-pairs across views and the nearest neighbors of instance-pairs within the view, respectively. By integrating global and local structure information within a unified framework, MSESC can learn a high-quality shared affinity matrix for better clustering performance. Extensive experimental results indicate the superiority of MSESC compared to several state-of-the-art methods.