Joint sparse principal component analysis
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- 作者：Shuangyan YiaAuthor Vitae ; Zhihui Laib ; lai_zhi_hui@163.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Zhenyu Hea ; zyhe@hitsz.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Yiu-ming Cheungc ; d ; ymc@comp.hkbu.edu.hk" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Yang Liuc ; d ; csygliu@comp.hkbu.edu.hk" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Dimensionality reduction ; Joint sparse ; ℓ2 ; 1ℓ2 ; 1-norm
- 刊名：Pattern Recognition
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：61
- 期：Complete
- 页码：524-536
- 全文大小：4098 K
文摘
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The first contribution is JSPCA relaxes the orthogonal constraint to freely select the useful features.
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The second contribution is JSPCA integrates feature selection into subspace learning via joint l2,1-norms.
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The third contribution is JSPCA provides a simple yet effective optimization algorithm and a series of theoretical analyses.
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