Multi-view Consistency Learning with Block Diagonal Guidance

Graph-based multi-view clustering methods are widely explored. However, there are still two problems with existing methods: 1) Although some methods divide the similarity matrix into a consistency matrix and an inconsistency matrix, it is difficult to deal with the consistency information that has been misclassified in the inconsistency matrix, resulting in insufficient extraction of the valid information; and 2) Although some methods obtain a unified similarity matrix with a block diagonal structure, they do not remove redundancy information from the unified similarity matrix. To address these two issues, this paper proposes a Multi-view Consistency Learning with Block Diagonal Guidance (MCLBDG) method. First, we obtain a similarity matrix for each view via low rank representation and adaptive neighborhood. Second, we divide the similarity matrix of each view into a consistency matrix and an inconsistency matrix. The inconsistency part of different views is sieved via Hadamard product. During iterations, the misclassified consistency part can be gradually extracted from the inconsistency information. In addition, block diagonal guidance is proposed to remove the redundancy information in the unified similarity matrix as much as possible, which reduces the interference of extra-cluster samples. Finally, spectral clustering is incorporated into the model to obtain clustering results directly. Comparative experimental results on the commonly used datasets demonstrate the superiority of the method over the existing methods.