Sparse maximum margin discriminant analysis for gene selection

Authors:
Yan Cui;Jian Yang;Chun-Hou Zheng
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu, China;College of Information and Communication Technology, Qufu Normal University, Rizhao, Shandong, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

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Abstract

Dimensionality reduction is necessary for gene expression data classification. In this paper, based on sparse representation, we propose a sparse maximum margin discriminant analysis (SMMDA) method for reducing the dimensionality of gene expression data. It could find the one dimension projection in the most separable direction of gene expression data, thus one can use sparse representation technique to regress the projection to obtain the relevance vector for the gene set and select genes according to the vector. Extensive experiments on publicly available gene expression datasets show that SMMDA is efficient for gene selection.