Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (4): 138-146.doi: 10.11896/jsjkx.240100131

• Database & Big Data & Data Science • Previous Articles     Next Articles

Consistent Block Diagonal and Exclusive Multi-view Subspace Clustering

WU Jie, WAN Yuan, LIU Qiujie   

  1. School of Science,Wuhan University of Technology,Wuhan 430070,China
  • Received:2024-01-16 Revised:2024-06-15 Online:2025-04-15 Published:2025-04-14
  • About author:WU Jie,born in 1999,postgraduate.His main research interests include machine learning and pattern recognition.
    WAN Yuan,born in 1976,Ph.D,professor.Her main research interests include data mining,pattern recognition,manifold learning,and machine learning.
  • Supported by:
    Fundamental Research Funds for the Central Universities of Ministry of Education of China(2021III030JC).

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Abstract: Subspace clustering method provides an effective solution to the clustering problem of high-dimensional multi-view data.Focusing on the issue that the representation matrix cannot obey the block diagonal property directly by using low rank or sparse constraints in existing algorithms,a consistent block diagonal and exclusive multi-view subspace clustering(CBDE-MSC) is proposed.CBDE-MSCdecomposes the subspace representation matrix of each perspective into consistent and specific self-representation matrices.For the consistent self-representation matrix,block diagonal constraint is used to make it have an approximate block diagonal structure and explore the consistency of the data.The exclusive constraint is applied between specific self-representation matrices to explore the complementarity of data.The matrix L2,1 norm is used to constrain the error matrix so that it satisfies row sparsity.In addition,alternate direction multiplier method(ADMM) is used to optimize the objective function.CBDE-MSC is evaluated by normalized mutual information(NMI),accuracy(ACC),adjusted rand index(AR) and F-score.Expe-rimental results show that compared with some existing excellent algorithms,CBDE-MSC has a great improvement in the results of the four indicators,especially in the YaleB dataset,CBDE-MSC compared with the classical method CSMSC,NMI,ACC,AR and F-score increased by 0.088,0.127,0.145 and 0.122,which verifies the effectiveness of the proposed algorithm.

Key words: Subspace clustering, Multi-view learning, Representation learning, Block diagonal representation

CLC Number: 

  • TP391
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