Symmetric low-rank representation for subspace clustering
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- 作者：Jie ChenAuthor Vitae ; Haixian ZhangAuthor Vitae ; Hua MaoAuthor Vitae ; Yongsheng SangAuthor Vitae ; Zhang Yi ; zhangyi@scu.edu.cn" class="auth_mail" title="E-mail the corresponding author ; zyiscu@gmail.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Subspace clustering ; Spectral clustering ; Symmetric low-rank representation ; Affinity matrix ; Low-rank matrix recovery ; Dimension reduction
- 刊名：Neurocomputing
- 出版年：2016
- 出版时间：15 January 2016
- 年：2016
- 卷：173
- 期：part_P3
- 页码：1192-1202
- 全文大小：1466 K
文摘
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A symmetric low-rank representation method for subspace clustering is proposed.
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A collaborative representation and dimension reduction are combined in learning.
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The symmetric low-rank representation can be calculated as a closed form solution.
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The symmetric low-rank representation maintains the subspace structures of data.
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The proposed method outperforms state-of-the-art subspace clustering algorithms.