Beyond low-rank representations: orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering

Wang, Yang and Wu, Lin (2018) Beyond low-rank representations: orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering. Neural Networks, 103 1-8. doi:10.1016/j.neunet.2018.03.006

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Author Wang, Yang
Wu, Lin
Title Beyond low-rank representations: orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering
Journal name Neural Networks   Check publisher's open access policy
ISSN 1879-2782
0893-6080
Publication date 2018-07-01
Sub-type Article (original research)
DOI 10.1016/j.neunet.2018.03.006
Open Access Status File (Author Post-print)
Volume 103
Start page 1
End page 8
Total pages 8
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon Press
Language eng
Abstract Low-Rank Representation (LRR) is arguably one of the most powerful paradigms for Multi-view spectral clustering, which elegantly encodes the multi-view local graph/manifold structures into an intrinsic low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts. In this paper we revisit it with a fundamentally different perspective by discovering LRR as essentially a latent clustered orthogonal projection based representation winged with an optimized local graph structure for spectral clustering; each column of the representation is fundamentally a cluster basis orthogonal to others to indicate its members, which intuitively projects the view-specific feature representation to be the one spanned by all orthogonal basis to characterize the cluster structures. Upon this finding, we propose our technique with the following: (1) We decompose LRR into latent clustered orthogonal representation via low-rank matrix factorization, to encode the more flexible cluster structures than LRR over primal data objects; (2) We convert the problem of LRR into that of simultaneously learning orthogonal clustered representation and optimized local graph structure for each view; (3) The learned orthogonal clustered representations and local graph structures enjoy the same magnitude for multi-view, so that the ideal multi-view consensus can be readily achieved. The experiments over multi-view datasets validate its superiority, especially over recent state-of-the-art LRR models.
Keyword Clustering
Low-Rank representation
Multi-view subspace learning
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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Created: Wed, 11 Apr 2018, 10:03:23 EST