Journal
KNOWLEDGE-BASED SYSTEMS
Volume 194, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2020.105514
Keywords
Multi-view clustering; Subspace clustering; Bilinear factorization; Low-rank representation
Categories
Funding
- National Natural Science Foundation of China [61573273, 61773208]
- Natural Science Foundation of Jiangsu Province [BK20191287]
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Multi-view clustering is an important and fundamental problem. Many multi-view subspace clustering methods have been proposed, and most of them assume that all views share a same coefficient matrix. However, the underlying information of multi-view data are not fully exploited under this assumption, since the coefficient matrices of different views should have the same clustering properties rather than be uniform among multiple views. To this end, this paper proposes a novel Constrained Bilinear Factorization Multi-view Subspace Clustering (CBF-MSC) method. Specifically, the bilinear factorization with an orthonormality constraint and a low-rank constraint is imposed for all coefficient matrices to make them have the same trace-norm instead of being equivalent, so as to explore the consensus information of multi-view data more fully. Finally, an Augmented Lagrangian Multiplier (ALM) based algorithm is designed to optimize the objective function. Comprehensive experiments tested on nine benchmark datasets validate the effectiveness and competitiveness of the proposed approach compared with several state-of-the-arts. (C) 2020 Elsevier B.V. All rights reserved.
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