Multi-view clustering via dual-norm and HSIC

Fully capturing valid complementary information in multi-view data enhances the connection between similar data points and weakens the correlation between different data point categories. In this paper, we propose a new multi-view clustering via dual-norm and Hilbert-Schmidt independence criterion (HSIC) induction (MCDHSIC) approach, which can enhance the complementarity, reduce the redundancy between multi-view representations, and improve the accuracy of the clustering results. This model uses the HSIC as the diversity regularization term to capture the nonlinear relationship between different views. In addition, l(1)-norm and Frobenius norm constraints are imposed to obtain a subspace representation matrix with inter-class sparsity and intra-class consistency. Moreover, we also designed a valuable approach to optimizing the proposed model and theoretically analyzing the convergence of the MCDHSIC method. The results of extensive experiments conducted on five challenging data sets show that the proposed method objectively achieves a highly competent performance compared with several other state-of-the-art multi-view clustering methods.