Entropy-based multi-view matrix completion for clustering with side information

Multi-view clustering aims to group multi-view samples into different clusters based on the similarity. Since side information can describe the relation between samples, for example, must-links and cannot-links, thus multi-view clustering with the consideration about side information along with samples can get more feasible clustering results. As a recent developed multi-view clustering approach, multi-view matrix completion (MVMC) constructs similarity matrix for each view and casts clustering into a matrix completion problem. Different from traditional multi-view clustering approaches, MVMC enforces the consistency of clustering results on different views as constraints for alternative optimization and the global optimal solution can be obtained. Although related experiments show that MVMC exhibits impressive performance, it still neglects the possibility of a sample belonging to a cluster. In this paper, we consider the possibility on the base of entropy and develop an entropy-based multi-view matrix completion for clustering with side information (EMVMC). Experiments on multi-view datasets Course, Citeseer, Cora, WebKB, NewsGroup, and Reuters validate the effectiveness of EMVMC.