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Beyond Low-Rank Representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering
| Content Provider | Scilit |
|---|---|
| Author | Wang, Yang Wu, Lin |
| Copyright Year | 2018 |
| Description | Journal: Neural Networks 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 followings: (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. |
| Related Links | http://arxiv.org/pdf/1708.02288 |
| Ending Page | 8 |
| Page Count | 8 |
| Starting Page | 1 |
| ISSN | 08936080 |
| DOI | 10.1016/j.neunet.2018.03.006 |
| Journal | Neural Networks |
| Volume Number | 103 |
| Language | English |
| Publisher | Elsevier BV |
| Publisher Date | 2018-07-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Neural Networks Information Systems Low-rank Representation Multi-view Subspace Learning |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Cognitive Neuroscience |
