Classification and saliency detection by semi-supervised low-rank representation
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- 作者：Miaoyun Zhao ; miaoyun_zhao@163.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Licheng Jiao lchjiao@mail.xidian.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Wenping Ma wpma@mail.xidian.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Hongying Liu hyliu@xidian.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Shuyuan Yang syyang@xidian.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Low rank representation ; Semi-supervised learning ; Outlier detection ; Saliency detection
- 刊名：Pattern Recognition
- 出版年：2016
- 出版时间：March 2016
- 年：2016
- 卷：51
- 期：Complete
- 页码：281-294
- 全文大小：3002 K
文摘
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A semi-supervised version of low rank representation is presented.
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Label information based outlier and saliency detection strategies are presented.
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Semi-supervised low rank learning is efficient and effective in classification.
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Semi-supervised low rank learning is reliable in outlier detection.