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Variable selection in discriminant analysis based on Gram-Schmidt process

Huiwen Wang 1 Meiling Chen 1 Gilbert Saporta 2 
2 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : 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.
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Submitted on : Monday, March 16, 2020 - 2:47:29 PM
Last modification on : Friday, August 5, 2022 - 2:54:00 PM
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  • HAL Id : hal-02507976, version 1



Huiwen Wang, Meiling Chen, Gilbert Saporta. Variable selection in discriminant analysis based on Gram-Schmidt process. Journal of Beijing University of Aeronautics and Astronautics, Beijing University of Aeronautics and Astronautics, 2011, 37 (8), pp.958-961. ⟨hal-02507976⟩



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