A modification of kernel discriminant analysis for high-dimensional data-with application to face recognition

Authors:
Dake Zhou;Zhenmin Tang
Affiliations:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, China
Venue:
Signal Processing
Year:
2010

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Abstract

Kernel discriminant analysis (KDA) is an effective statistical method for dimensionality reduction and feature extraction. However, traditional KDA methods suffer from the small sample size problem. Moreover, they endure the Fisher criterion that is nonoptimal with respect to classification rate. This paper presents a variant of KDA that deals with both of the shortcomings in an efficient and cost effective manner. The key to the approach is to use simultaneous diagonalization technique for optimization and meanwhile utilize a modified Fisher criterion that it is more closely related to classification error. Extensive experiments on face recognition task show that the proposed method is an effective nonlinear feature extractor.