Sparsity optimization method for multivariate feature screening for gene expression analysis

Constructing features from high-dimensional gene expression data is a critically important task for monitoring and predicting patients' diseases, or for knowledge discovery in computational molecular biology. The features need to capture the essential characteristics of the data to be maximally distinguishable. Moreover, the essential features usually lie in small or extremely low-dimensional subspaces, and it is crucial to find them for knowledge discovery and pattern classification. We present a computational method for extracting small or even extremely low-dimensional subspaces for multivariate feature screening and gene expression analysis using sparse optimization techniques. After we transform the feature screening problem into a convex optimization problem, we develop an efficient primal-dual interior-point method expressively for solving large-scale problems. The effectiveness of our method is confirmed by our experimental results. The computer programs will be publicly available.