Deep low-rank subspace ensemble for multi-view clustering

Abstract

Multi-view clustering aims to incorporate complementary information from different data views for more effective clustering. However, it is difficult to obtain the true categories of data based on complex distribution and diversified latent attributes of multi-view data. In this paper, we propose a new multi-view clustering method that integrates deep matrix factorization, low-rank subspace learning, and multiple subspace ensemble in a unified framework, which we term as the Deep Low-Rank Subspace Ensemble (DLRSE) method. DLRSE first employs deep matrix factorization to capture diverse and hierarchical structures in data for robust multi-view multi-layer data representations. Then, low-rank subspace representations are learned from the extracted factors to further reveal their correlations, from which more explicit clustering structure can be obtained. We further develop a subspace ensemble learning method with structured sparsity regularization to aggregate different subspaces into a consensus subspace, which can incorporate the intrinsic clustering structure across multiple views better. Extensive experiments on several datasets demonstrate that the proposed method can effectively explore the diversified clustering structure inherent in data and exploit multi-view complementary information, and achieve considerable improvement in performance over existing methods.