Symmetric low-rank representation for subspace clustering

Chen, Jie, Zhang, Haixian, Mao, Hua, Sang, Yongsheng and Yi, Zhang (2016) Symmetric low-rank representation for subspace clustering. Neurocomputing, 173 (3). pp. 1192-1202. ISSN 0925-2312

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Chen et al - Symmetric low-rank representation for subspace clustering AAM.pdf - Accepted Version

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Official URL: http://dx.doi.org/10.1016/j.neucom.2015.08.077

Abstract

We propose a symmetric low-rank representation (SLRR) method for subspace clustering, which assumes that a data set is approximately drawn from the union of multiple subspaces. The proposed technique can reveal the membership of multiple subspaces through the self-expressiveness property of the data. In particular, the SLRR method considers a collaborative representation combined with low-rank matrix recovery techniques as a low-rank representation to learn a symmetric low-rank representation, which preserves the subspace structures of high-dimensional data. In contrast to performing iterative singular value decomposition in some existing low-rank representation based algorithms, the symmetric low-rank representation in the SLRR method can be calculated as a closed form solution by solving the symmetric low-rank optimization problem. By making use of the angular information of the principal directions of the symmetric low-rank representation, an affinity graph matrix is constructed for spectral clustering. Extensive experimental results show that it outperforms state-of-the-art subspace clustering algorithms.

Item Type: Article
Uncontrolled Keywords: Subspace clustering, Spectral clustering, Symmetric low-rank representation, Affinity matrix, Low-rank matrix recovery, Dimension reduction
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 13 Jun 2019 15:49
Last Modified: 11 Oct 2019 09:19
URI: http://nrl.northumbria.ac.uk/id/eprint/39684

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