Multi-view clustering via simultaneously learning shared subspace and affinity matrix

Abstract

Due to the existence of multiple views in many real-world data sets, multi-view clustering is increasingly popular. Many approaches have been investigated, among which the subspace clustering methods finding the underlying subspaces of data have been developed recently. Although the subspace-based multi-view methods can achieve promising performance, the shared subspace information has not been fully utilized. To address this problem, a novel multi-view clustering model by simultaneously learning shared subspace and affinity matrix is proposed. In our method, a shared subspace is learned to preserve the effective consensus information of all views. Then, a subspace-based affinity matrix with adaptive neighbors is learned to assign the most suitable cluster to each data point. An iterative strategy is developed for solving this problem. Moreover, experiments on four benchmark data sets demonstrate that our algorithm outperforms other state-of-the-art algorithms.