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������1, ��÷��1, Gilbert Saporta2*
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2. �������蹤�ռ���ѧԺ, ����75141
Variable selection in discriminant analysis based on Gram-Schmidt process
Wang Huiwen1, Chen Meiling1, Gilbert Saporta2*
1. School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;
2. Conservatoire National DesArts etMétier, Paris 75141, France

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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.
Keywords�� Gram-Schmidt orthogonal transformation   discriminant analysis   variable selection   multiple correlation     
Received 2010-04-13;

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About author: ������(1957-),Ů,�ӱ�������,����,wanghw@vip.sina.com.
������, ��÷��, Gilbert Saporta.����Gram-Schmidt���̵��б����ɸѡ����[J]  �������պ����ѧѧ��, 2011,V37(8): 958-961
Wang Huiwen, Chen Meiling, Gilbert Saporta.Variable selection in discriminant analysis based on Gram-Schmidt process[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2011,V37(8): 958-961
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