Subspace pursuit for gene profile classificaiton

Abstract

Gene profile classification is achieved by casting the classification problem as finding the sparse representation of testing samples with respect to training samples. The sparse representation is found by subspace pursuit, which is much more efficient than linear programming techniques. The new approach, with no need of model selection, however, still has the performance which can match the best result achieved among all the SVM variants after careful model selection.