Adaptive Graph Learning for Supervised Low-Rank Spectral Feature Selection

Abstract

Spectral feature selection (SFS) is getting more and more attention in recent years. However, conventional SFS has some weaknesses that may corrupt the performance of feature selection, since (1) SFS generally preserves the either global structure or local structure, which can’t provide comprehensive information for the model; (2) graph learning and feature selection of SFS is two individual processes, which is hard to achieve the global optimization. Thus, a novel SFS is proposed via introducing a low-rank constraint for capturing inherent structure of data, and utilizing an adaptive graph learning to couple the graph learning and feature data learning in an iterative framework to output a robust and accurate learning model. A optimization algorithm is proposed to solve the proposed problem with a fast convergence. By comparing to some classical and first-class feature selection methods, our method has exhibited a competitive performance.