Symmetric low-rank representation for subspace clustering

Jie Chen, Haixian Zhang, Hua Mao, Yongsheng Sang, Zhang Yi

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17 Citations (Scopus)
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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.
Original languageEnglish
Pages (from-to)1192-1202
Number of pages11
JournalNeurocomputing
Volume173
Issue number3
Early online date6 Sep 2015
DOIs
Publication statusPublished - 15 Jan 2016

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