Kernel-based improved discriminant analysis and its application to face recognition |
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Authors: | Dake Zhou Zhenmin Tang |
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Affiliation: | (1) College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 210016 Nanjing, China;(2) Department of Computer Science, Nanjing University of Science and Technology, 210094 Nanjing, China |
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Abstract: | Kernel discriminant analysis (KDA) is a widely used tool in feature extraction community. However, for high-dimensional multi-class
tasks such as face recognition, traditional KDA algorithms have the limitation that the Fisher criterion is nonoptimal with
respect to classification rate. Moreover, they suffer from the small sample size problem. This paper presents a variant of
KDA called kernel-based improved discriminant analysis (KIDA), which can effectively deal with the above two problems. In
the proposed framework, origin samples are projected firstly into a feature space by an implicit nonlinear mapping. After
reconstructing between-class scatter matrix in the feature space by weighted schemes, the kernel method is used to obtain
a modified Fisher criterion directly related to classification error. Finally, simultaneous diagonalization technique is employed
to find lower-dimensional nonlinear features with significant discriminant power. Experiments on face recognition task show
that the proposed method is superior to the traditional KDA and LDA. |
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Keywords: | |
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