Discriminative sparse subspace learning and its application to unsupervised feature selection.
In order to efficiently use the intrinsic data information, in this study a Discriminative Sparse Subspace Learning (DSSL) model has been investigated for unsupervised feature selection. First, the feature selection problem is formulated as a subspace learning problem. In order to efficiently learn the discriminative subspace, we investigate the discriminative information in the subspace learning process. Second, a two step TDSSL algorithm and a joint modeling JDSSL algorithm are developed to incorporate the clusters' assignment as the discriminative information. Then, a convergence analysis of these two algorithms is provided. A kernelized discriminative sparse subspace learning (KDSSL) method is proposed to handle the nonlinear subspace learning problem. Finally, extensive experiments are conducted on real world datasets to show the superiority of the proposed approaches over several state of the art approaches.
ISA transactions