Comprehensive multi-view self-representations for clustering

doi:10.1016/j.eswa.2024.124103

Comprehensive multi-view self-representations for clustering

2024-10-01

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DOI：10.1016/j.eswa.2024.124103

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Subspace learning-based methods have shown excellent performance for multi-view clustering, yet have the following problems: (1) most existing methods obtain the subspace representation from the original space, which might contain noises and cannot guarantee a clean enough subspace representation; (2) existing methods mainly focus on the consistency of the subspace representation, while the unique information of each view is not sufficiently exploited. To solve these two problems, we propose a novel multi-view subspace clustering method called comprehensive multi-view self-representations (CMSR). Specifically, we learn the original coefficient matrix of each view through the self-representation, which can reduce the noise of the original space to some extent. Then, we learn the subspace representation of the original coefficient matrix and decompose it into a consistent coefficient matrix and multiple diverse coefficient matrices, which can exploit the consistent and complementary information of multi-view data. Further, we impose the Schatten p -norm constraint on the consistent coefficient matrix to capture robust consistent information. Finally, the comprehensive results on eight real datasets demonstrate the versatility and effectiveness of the proposed method.

Keywords:

Multi-view clustering

Subspace clustering

Consistency

Diversity

Schatten p-norm

Comprehensive multi-view self-representations for clustering

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