Low-rank tensor multi-view subspace clustering via cooperative regularization

In order to explore the importance of the hypergraph regularization and the Tikhonov regularization in multi-view clustering, this paper proposes a novel multi-view clustering model, termed as low-rank tensor multi-view subspace clustering via collaborative regularization (LT-MSCCR). The LT-MSCCR model introduces the idea of tensor. The tensor is designed to represent the result of superimposing the subspace representation matrix, which is used to explore the high order correlations between different views, and the low-rank restriction is performed for this tensor to effectively reduce redundant information. Furthermore, we adopt the hypergraph regularization to mine effective geometric information in multi-view data. Meanwhile, we also impose the Tikhonov regularization constraint on the subspace representation matrix so as to improve the smoothness of the subspace representation matrix and enhance the recognition performance of the proposed method. In addition, we also designed a valuable approach to optimizing the proposed model and theoretically analyzing the convergence of the LT-MSCCR method. The experimental results on some datasets show that the proposed model is better than many advanced multi-view clustering methods.