View-specific anchors coupled tensorial bipartite graph learning for incomplete multi-view clustering

The incomplete multi -view clustering (IMVC) aims to explore the consensus and complementary information embedded in incomplete multi -view data, so as to accurately aggregate all samples into different clusters. Existing IMVC methods still have the following issues that need to be further solved: how to efficiently (1) alleviate the computational complexity; (2) capture the complementary information from views; (3) uncover the high -order correlations underlying incomplete multi -view data. To address these problems, a new method, called view -specific anchors coupled tensorial bipartite graph learning for incomplete multi -view clustering (VATBGIMC), is proposed in this paper. Specifically, view -specific anchors are learned from nonmissing samples to represent the distribution of all samples including missing ones. With the help of high -quality anchors, the bipartite graph between all samples and anchors is constructed, which can preserve the complementary information. Meanwhile, these bipartite graphs are stacked into a tensor which is imposed with a low -rank constraint to adequately capture the inter -view and inter -sample correlations from incomplete multi -view data. Further, the augmented graph of bipartite graph is constrained by Laplacian rank to learn a consensus cluster indicator. A large number of experiments validate the superiority and efficiency of the proposed method in terms of clustering performance and time consumption.