Hyper-Laplacian regularized multi-view subspace clustering with low-rank tensor constraint

Abstract In this paper, we propose a novel hyper-Laplacian regularized multiview subspace clustering with low-rank tensor constraint method, which is referred as HLR-MSCLRT. In the HLR-MSCLRT model, the subspace representation matrices of different views are stacked as a tensor, and then the high order correlations among data can be captured. To reduce the redundancy information of the learned subspace representations, a low-rank constraint is adopted to the constructed tensor. Since data in the real world often reside in multiple nonlinear subspaces, the HLR-MSCLRT model utilizes the hyper-Laplacian graph regularization to preserve the local geometry structure embedded in a high-dimensional ambient space. An efficient algorithm is also presented to solve the optimization problem of the HLR-MSCLRT model. The experimental results on some data sets show that the proposed HLR-MSCLRT model outperforms many state-of-the-art multi-view clustering approaches.