Scalable tri-factorization guided multi-view subspace clustering

doi:10.1016/j.knosys.2025.113119

Scalable tri-factorization guided multi-view subspace clustering

2025-03-01

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DOI：10.1016/j.knosys.2025.113119

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Anchor-based Multi-view Subspace Clustering (AMSC) has exhibited its outstanding capability in large-scale multi-view clustering. Despite significant progress, previous AMSC approaches still suffer from two limitations. First, they mostly neglect the high-order correlation, which undermines their ability in discovering complex cluster structures. Second, they frequently overlook the potential connection between multi-view dimension reduction and anchor subspace clustering, which affects their robustness to low-quality views. In view of these issues, we present a Scalable Tri-factorization Guided Multi-view Subspace Clustering (ST-MSC) approach. Specifically, the proposed approach seeks to recover the latent sample-anchor relationships in multiple embedded spaces, where the multi-view anchor representations are stacked into a low-rank tensor to enhance their high-order correlations with tri-factorization guidance. Theoretical analysis indicates that the tri-factorization paradigm has inherent relevance with two mutually beneficial tasks, namely, the multi-view dimensionality reduction and the anchor-based multi-view subspace clustering. Furthermore, a simple yet fast algorithm is devised to minimize the objective model, where the latent embedding spaces and the anchor subspace structure can be iteratively updated in a unified manner. Experiments have been conducted to verify the effectiveness and efficiency of our ST-MSC approach in comparison with the advanced approaches.

Keywords:

Data clustering

Multi-view clustering

Anchor-based multi-view subspace clustering

Tri-factorization

Low-rank tensor learning

Scalable tri-factorization guided multi-view subspace clustering

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