JSM 2004 - Toronto

Abstract #301272

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Activity Number: 90
Type: Contributed
Date/Time: Monday, August 9, 2004 : 9:00 AM to 10:50 AM
Sponsor: Biometrics Section
Abstract - #301272
Title: A Convenient Kernel Fisher Discriminant
Author(s): Mei-Hsien Lee*+
Companies: National Taiwan University
Address: 6F No. 24 Gongyuan Rd., Banciao, Taipei, 220, , Taiwan, ROC
Keywords: classification ; Fisher linear discriminant analysis ; regularized discriminant analysis ; reproducing kernel ; reproducing kernel Hilbert space ; support vector machine
Abstract:

The Fisher linear discriminant has long been a popular tool for classification. Recently, it has been extended via kernel machine to nonlinear discriminant. Such an extension of the classical Fisher linear discriminant to the kernel Fisher discriminant involves maximizing a regularized Rayleigh coefficient, which requires some computational efforts. We propose a convenient version of kernel Fisher discriminant (KFD), which does not involve any optimization, but only simple operations of addition and subtraction of kernel values. We also show that this convenient version of KFD is a criterion based on likelihood ratio of two Gaussians on a reproducing kernel Hilbert space. The proposed method is test on simulated data as well as real data.

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