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

Xiuqin Deng, Yifei Zhang and Fangqing Gu ()
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Xiuqin Deng: School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510520, China
Yifei Zhang: School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510520, China
Fangqing Gu: School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510520, China

Mathematics, 2023, vol. 11, issue 6, 1-15

Abstract: 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.

Keywords: multi-view subspace clustering; spectral clustering; feature concatenation; information enhancement; information fusion; consensus matrix (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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