Regularized coplanar discriminant analysis for dimensionality reduction
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- 作者：Ke-Kun Huanga ; kkcocoon@163.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Dao-Qing Daib ; stsddq@mail.sysu.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Chuan-Xian Renb ; rchuanx@mail.sysu.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Dimensionality reduction ; Sparse representation classifier ; Face recognition ; Hyperspectral image classification ; Coplanar discriminant analysis
- 刊名：Pattern Recognition
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：62
- 期：Complete
- 页码：87-98
- 全文大小：1550 K
文摘
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RCDA simultaneously finds a projection matrix and linear representation coefficients.
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RCDA makes the samples from the same class coplanar.
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The linear representation coefficients are regularized by the proposed mean L2 norm.
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An optimization algorithm is proposed to solve the model of RCDA.