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)
Chair
Vice Chair
Secretary
Assistant

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