Enhanced tensor multi-view clustering via dual constraints

Abstract

Multi-view clustering is to divide data into distinct clusters according to their different features. Tensor-based multi-view clustering can capture higher order connections between various views for better clustering results. However, they have limitations: (1) the higher-order local geometric structure in non-linear subspaces is not considered; (2) significant differences in singular values are not reflected. To resolve the above issues, we introduce a novel method called Enhanced Tensor Multi-view Clustering via Dual Constraints(ETMC-DC). ETMC-DC utilizes Hyper-Laplacian regularization to maintain higher-order local geometric structure in the raw space. The Schatten-p norm is used to the tensor stacked by the obtained affinity matrix to process unequal singular values, and larger singular values carry more structural information, and vice versa. Moreover, the complexity of the model is reduced by the rotation of the tensors constructed from Markov transition probabilities. Finally, an iterative update technique is used for optimizing the presented ETMC-DC. We have conducted extensive experiments on real-world datasets in various forms to demonstrate that ETMC-DC can perform exceptionally well in comparison to other multi-view clustering approaches.