Presentation 2003/10/16
Generalization of Kernel PCA and Automatic Parameter Tuning
Takahide NOGAYAMA, Haruhisa TAKAHASHI, Masakazu MURAMATSU,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) We propose a generalized kernel PCA which provides much more accuracy information of kernel space. Calculating partial derivatives of eigenvalues with kernel parameters, we can obtain the optimal kernel parameters. The criterion for optimal parameters are given by a quadratic cost function with respect to eigenvalues. We compared our method with SVM for face recognition, and showed that our method works efficiently as expected.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Pattern Recognition / Kernel Method / Principal Component Analysis / Support Vector Machine
Paper # PRMU2003-122,NC2003-53
Date of Issue

Conference Information
Committee NC
Conference Date 2003/10/16(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Vice Chair

Paper Information
Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Generalization of Kernel PCA and Automatic Parameter Tuning
Sub Title (in English)
Keyword(1) Pattern Recognition
Keyword(2) Kernel Method
Keyword(3) Principal Component Analysis
Keyword(4) Support Vector Machine
1st Author's Name Takahide NOGAYAMA
1st Author's Affiliation Graduate School of Electro-Communications, The University of Electro-Communications()
2nd Author's Name Haruhisa TAKAHASHI
2nd Author's Affiliation The University of Electro-Communications
3rd Author's Name Masakazu MURAMATSU
3rd Author's Affiliation The University of Electro-Communications
Date 2003/10/16
Paper # PRMU2003-122,NC2003-53
Volume (vol) vol.103
Number (no) 391
Page pp.pp.-
#Pages 6
Date of Issue