Sparse Low-Rank and Graph Structure Learning for Supervised Feature Selection

Abstract

Spectral feature selection (SFS) is superior to conventional feature selection methods in many aspects, by extra importing a graph matrix to preserve the subspace structure of data. However, the graph matrix of classical SFS that is generally constructed by original data easily outputs a suboptimal performance of feature selection because of the redundancy. To address this, this paper proposes a novel feature selection method via coupling the graph matrix learning and feature data learning into a unified framework, where both steps can be iteratively update until achieving the stable solution. We also apply a low-rank constraint to obtain the intrinsic structure of data to improve the robustness of learning model. Besides, an optimization algorithm is proposed to solve the proposed problem and to have fast convergence. Compared to classical and state-of-the-art feature selection methods, the proposed method achieved the competitive results on twelve real data sets.