Feature Selection Based on Linear Discriminant Analysis
Fengxi Song, Dayong Mei, Hongfeng Li · 2010
In this paper we propose a novel feature selection method based on linear discriminant analysis (LDA). To view feature selection as a numerical computation problem, the paper shows, for the first time, that it is feasible to employ LDA for feature selection. The proposed method also shows that different components statistically have different effects on the feature selection result, which can be evaluated by the components of the eigenvector. As there are multiple eigenvectors, the proposed method takes a small number of eigenvectors into account when evaluating the effect of the component of the sample data. The experimental results on face recognition show that the proposed method is not only able to greatly reduce the dimensionality of the original samples, but also able to yield promising classification accuracies.