Consistent and Specific Multi-View Subspace Clustering

Authors

  • Shirui Luo Chinese Academy of Sciences; University of Chinese Academy of Sciences; Institute of Information Engineering; School of Cyber Security
  • Changqing Zhang Tianjin University
  • Wei Zhang Chinese Academy of Sciences; Institute of Information Engineering
  • Xiaochun Cao Chinese Academy of Sciences; University of Chinese Academy of Sciences; Institute of Information Engineering; School of Cyber Security

Keywords:

Multi-view learning, Subspace clustering

Abstract

Multi-view clustering has attracted intensive attention due to the effectiveness of exploiting multiple views of data. However, most existing multi-view clustering methods only aim to explore the consistency or enhance the diversity of different views. In this paper, we propose a novel multi-view subspace clustering method (CSMSC), where consistency and specificity are jointly exploited for subspace representation learning. We formulate the multi-view self-representation property using a shared consistent representation and a set of specific representations, which better fits the real-world datasets. Specifically, consistency models the common properties among all views, while specificity captures the inherent difference in each view. In addition, to optimize the non-convex problem, we introduce a convex relaxation and develop an alternating optimization algorithm to recover the corresponding data representations. Experimental evaluations on four benchmark datasets demonstrate that the proposed approach achieves better performance over several state-of-the-arts.

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Published

2018-04-29

How to Cite

Luo, S., Zhang, C., Zhang, W., & Cao, X. (2018). Consistent and Specific Multi-View Subspace Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11617