[WCS11] Variable selection in discriminant analysis based on Gram-Schmidt process

Revue Nationale avec comité de lecture : Journal Journal of Beijing University of Aeronautics and Astronautics, vol. 37(8), pp. 958-961, 2011

Mots clés: Gram-Schmidt orthogonal transformation discriminant analysis variable selection multiple correlation

Résumé: A new linear discriminant analysis modeling method based on Gram-Schmidt process was introduced, which firstly selected the most effective variables for classification in the independent variables set. In the meantime, the insignificant variables and the redundant information were identified and removed from the independent variables set. The selected variables were transformed into a set of orthogonal vectors by Gram-Schmidt process. Not only can the proposed method accomplish variable selection in linear discrimination, but also overcome the multi-collinearity problem effectively. Since F-statistic works as a criterion to verify the discrimination effect of each selected variable, it helps analysts to understand the analysis result. In order to test the reasonableness and effectiveness of the method, a simulation experiment was carried out. The result indicates that the proposed method can lead to a reasonable and precise conclusion.

Equipe: msdma


@article {
title="{Variable selection in discriminant analysis based on Gram-Schmidt process}",
author="H. Wang and M. Chen and G. Saporta",
journal="Journal of Beijing University of Aeronautics and Astronautics",