The analysis of microarray gene expression data to obtain useful information is a challenging problem in bioinformatics. Feature selection is an efficient computational technique in processing the analysis of high-dimensional microarray data. Due to the lack of label information in practice, unsupervised feature selection is considered to be more practically important and correspondingly more difficult. In this paper, we propose a novel unsupervised feature selection method, which utilizes local regression and discriminant analysis for structure learning on microarray gene expression data. By imposing row sparsity on the weight matrix through l2,1-norm regularization, the proposed method optimizes for selecting the discriminative genes which are more informative and better capture the interesting natural classes of samples. We develop an effective algorithm to solve the l2,1-norm-based optimization problem in our method and present the convergence analysis. Finally, we evaluate the proposed method on real microarray gene expression datasets. The experimental results demonstrate that the proposed method not only achieves good performance, but also outperforms other state-of-the-art unsupervised feature selection methods.

unsupervised feature selection, structure learning, local regression, discriminant analysis, gene selection, microarray gene expression data