Volume 37 Issue 8
Aug.  2011
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Wang Huiwen, Chen Meiling, Gilbert Saportaet al. Variable selection in discriminant analysis based on Gram-Schmidt process[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(8): 958-961. (in Chinese)
Citation: Wang Huiwen, Chen Meiling, Gilbert Saportaet al. Variable selection in discriminant analysis based on Gram-Schmidt process[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(8): 958-961. (in Chinese)

Variable selection in discriminant analysis based on Gram-Schmidt process

  • Received Date: 13 Apr 2010
  • Publish Date: 30 Aug 2011
  • 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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