Abstract: Aiming at the problem of how to fully integrate the complementary and diverse information of multi-view data to improve the clust
ering performance, we proposed a multi-view subspace clustering based on adaptive weighted consensus self-representation. Firstly, we introduced sparse mutual exclusion to learn view-specific sparse self-representation matrix, and then used adaptive weighted learning of multi-view consensus self-representation matrix to fuse the self-representation learned from various views. Secondly, we integrated the learning of multi-view consensus matrix and clustering indicator matrix into a unified optimization model, so that self-representation learning and clustering could promote each other. Finally, we conducted experiments on six commonly used multi-view datasets, and compared them with nine related methods. The experimental results show that the proposed method has obvious information fusion effect and improves clustering effect.

Key words: multi-view subspace clustering, sparse representation, self-representation, adaptive weighted learning