Optimal feature selection for sparse linear discriminant analysis and its applications in gene expression data

Publisher:
Elsevier
Publication Type:
Journal Article
Citation:
Computational Statistics and Data Analysis, 2013, 66 (1), pp. 140 - 149
Issue Date:
2013-01
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This work studies the theoretical rules of feature selection in linear discriminant analysis (LDA), and a new feature selection method is proposed for sparse linear discriminant analysis. An l1 minimization method is used to select the important features
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