Fusion and Enhancement of Consensus Matrix for Multi-View Subspace Clustering

Multi-view subspace clustering is an effective method that has been successfully applied to many applications and has attracted the attention of scholars. Existing multi-view subspace clustering seeks to learn multiple representations from different views, then gets a consistent matrix. Until now, most of the existing efforts only consider the multi-view information and ignore the feature concatenation. It may fail to explore their high correlation. Consequently, this paper proposes a multi-view subspace clustering algorithm with a novel consensus matrix construction strategy. It learns a consensus matrix by fusing the different information from multiple views and is enhanced by the information contained in the original feature direct linkage of the data. The error matrix of the feature concatenation data is reconstructed by regularization constraints and the sparse structure of the multi-view subspace. The feature concatenation data are simultaneously used to fuse the individual views and learn the consensus matrix. Finally, the data is clustered by using spectral clustering according to the consensus matrix. We compare the proposed algorithm with its counterparts on six datasets. Experimental results verify the effectiveness of the proposed algorithm.

Automated summary

Multi-view subspace clustering is an effective method that has been successfully applied in various applications and has attracted attention from scholars. However, most existing methods only focus on multi-view information and ignore feature concatenation, leading to a failure in exploring their high correlation. To address this, this paper proposes a novel consensus matrix construction strategy for multi-view subspace clustering. The proposed algorithm learns a consensus matrix by fusing information from multiple views and enhances it with the original feature direct linkage. Experimental results on six datasets demonstrate the effectiveness of the proposed algorithm.