Anchor-based multi-view subspace clustering with graph learning

Chao Su

doi:10.1016/j.neucom.2023.126320

Anchor-based multi-view subspace clustering with graph learning

2023-08-01

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DOI：10.1016/j.neucom.2023.126320

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Multi-view subspace clustering (MVSC) has been drawn wide attentions in the area of pattern recogni-tion and data mining. However, for a multi-view dataset with n samples and V views from k clusters, MVSC commonly requires O(Vn2) memory for storing the view-specific graph matrices and O(n3) time for the eigenvalue decomposition of a shared graph matrix. Hence, most of MVSC methods are difficult to handle the large-scale multi-view data problem. To address this issue, this paper proposes an Anchor-based Multi-View Subspace Clustering with Graph Learning (AMVSCGL) method. Instead of con-structing a n x n graph matrix, our method generates a shared coefficient matrix with the size of n x k based on few learned view-specific anchors. Moreover, through further merging a graph learning term, this shared coefficient matrix can simultaneously capture the global and local information among mul-tiple views and few learned view-specific anchors for clustering. Experimental results on seven large-scale multi-view data verify our AMVSCGL's effectiveness and superiority in comparison with some state-of-the-art methods.& COPY; 2023 Elsevier B.V. All rights reserved.

Keywords:

Multi-view data

Subspace clustering

Graph learning

Anchor learning

Anchor-based multi-view subspace clustering with graph learning

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