An efficient method for computing orthogonal discriminant vectors

Authors:
Jinghua Wang;Yong Xu;David Zhang;Jane You
Affiliations:
Biometrics Research Center, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, Guangdong 518055, People's Republic of China;Biometrics Research Center, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Biometrics Research Center, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Venue:
Neurocomputing
Year:
2010

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Abstract

We propose a linear discriminant analysis method. In this method, every discriminant vector, except for the first one, is worked out by maximizing a Fisher criterion defined in a transformed space which is the null space of the previously obtained discriminant vectors. All of these discriminant vectors are used for dimension reduction. We also propose two algorithms to implement the model. Based on the algorithms, we prove that the discriminant vectors will be orthogonal if the within-class scatter matrix is not singular. The experimental results show that the proposed method is effective and efficient.