Sparse Maximum Margin Discriminant Analysis for Gene Selection

摘要

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.