Consistent Block Diagonal and Exclusive Multi-view Subspace Clustering

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 bl...

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Bibliographic Details
Published inJi suan ji ke xue Vol. 52; no. 4; pp. 138 - 146
Main Author WU Jie, WAN Yuan, LIU Qiujie
Format Journal Article
LanguageChinese
Published Editorial office of Computer Science 01.04.2025
Subjects
subspace clustering|multi-view learning|representation learning|block diagonal representation
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ISSN1002-137X
DOI10.11896/jsjkx.240100131

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Summary: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 b
ISSN:1002-137X
DOI:10.11896/jsjkx.240100131