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Regularized linear discriminant analysis and its recursive implementation for gene subset selection

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3 Author(s)
Mao, K.Z. ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore ; Feng Yang ; Wenyin Tang

Although mostly used for pattern classification, linear discriminant analysis (LDA) may also be used for feature selection. When employed to select genes for microarray data, which has high dimensionality and small sample size, LDA encounters three problems, including singularity of scatter matrix, overfitting and prohibitive computational complexity. In this study, we propose a new regularization technique to address the singularity and overfitting problem. In addition, we develop a recursive implementation for LDA to reduce computational overhead. Experimental studies on 5 gene microarray problems show that the regularized linear discriminant analysis (RLDA) and its recursive implementation produce gene subsets with excellent classification performance.

Published in:

Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2011 IEEE Symposium on

Date of Conference:

11-15 April 2011