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17th International Conference on Pattern Recognition (ICPR'04) - Volume 2
A Kernel Fractional-Step Nonlinear Discriminant Analysis for Pattern Recognition
Cambridge UK
August 23-August 26
ISBN: 0-7695-2128-2
ASCII Text
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Guang Dai, Yuntao Qian, Sen Jia, "A Kernel Fractional-Step Nonlinear Discriminant Analysis for Pattern Recognition," Pattern Recognition, International Conference on, vol. 2, pp. 431-434, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 2, 2004.
 
 
BibTex
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@article{ 10.1109/ICPR.2004.1334246,
author = {Guang Dai and Yuntao Qian and Sen Jia},
title = {A Kernel Fractional-Step Nonlinear Discriminant Analysis for Pattern Recognition},
journal ={Pattern Recognition, International Conference on},
volume = {2},
year = {2004},
issn = {1051-4651},
pages = {431-434},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334246},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Pattern Recognition, International Conference on
TI - A Kernel Fractional-Step Nonlinear Discriminant Analysis for Pattern Recognition
SN - 1051-4651
SP431
EP434
A1 - Guang Dai,
A1 - Yuntao Qian,
A1 - Sen Jia,
PY - 2004
KW - Pattern recognition (PR)
KW - feature extraction
KW - kernel fractional-step nonlinear discriminant analysis (KF-NDA)
KW - direct fractional-step LDA (DF-LDA)
VL - 2
JA - Pattern Recognition, International Conference on
ER -
 
Guang Dai, Zhejiang University, Hangzhou, P.R. China
Yuntao Qian, Zhejiang University, Hangzhou, P.R. China
Sen Jia, Zhejiang University, Hangzhou, P.R. China
Feature extraction is one of the most significant and fundamental problems in pattern recognition (PR). This paper introduces a novel kernel fractional-step nonlinear discriminant analysis (KF-NDA) for feature extraction in PR. It not only overcomes the limitation of failing for a nonlinear problem in the direct fractional-step linear discriminant analysis (DF-LDA), but also improves the generalization ability of traditional kernel nonlinear discriminant analysis (K-NDA). It is then applied to an experiment on face recognition, and the results demonstrate that this method is more effective than the existing methods.
Index Terms:
Pattern recognition (PR), feature extraction, kernel fractional-step nonlinear discriminant analysis (KF-NDA), direct fractional-step LDA (DF-LDA)
Citation:
Guang Dai, Yuntao Qian, Sen Jia, "A Kernel Fractional-Step Nonlinear Discriminant Analysis for Pattern Recognition," icpr, vol. 2, pp.431-434, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 2, 2004
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