Feature selection based on linear discriminant analysis

Abstract

The paper proposed a new approach of feature selection based on Constrained Linear Discriminant Analysis(CLDA),which modeled feature selection as a search problem in subspace and made optimal solution subject to some restrictions.Furthermore,CLDA optimization problem was transformed into a process of scoring and sorting features.Experiments on UCI machine learning repository and Reuters-21578 dataset show that the proposed approach can consistently obtain better results with fewer features than that with all features.