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Paper Abstract and Keywords
Presentation 2011-11-09 15:45
Kernel and Feature Search in Kernel PCA
Alam Md. Ashad (SOKENDAI), Kenji Fukumizu (ISM) IBISML2011-49
Abstract (in Japanese) (See Japanese page) 
(in English) During the last decade, unsupervised learning has become an important application area of the kernel
methods. While the choice of kernel is essential in kernel methods for favorable performance, well-founded methods
for kernel choice in unsupervised learning have not yet been established. We proposes a method for choosing parameters,
the kernel and the number of features, of the kernel principal component analysis (kernel PCA) which is one
of the most powerful tools in unsupervised learning. While the cross-validation has been applied to the standard
PCA for choosing the number of components in kernel PCA we have a different feature space for each kernel, which
makes it difficult to apply the cross-validation on the feature spaces for seeking a kernel and the number features.
The propose method chooses them through the cross-validation based on pre-images. This paper mainly focuses two
examples of kernels, Gaussian radial basis function (RBF) kernel and polynomial kernel. We have made experiments
on simulated data as well as real world problems. The results show that the proposed method successfully chooses
an appropriate kernel and the number of features in kernel PCA in terms of visualization and classification errors
on the principal components.
Keyword (in Japanese) (See Japanese page) 
(in English) Unsupervised Learning / Kernel PCA / Pre-Iamges / Leave-one-out Cross-Validation / / / /  
Reference Info. IEICE Tech. Rep., vol. 111, no. 275, IBISML2011-49, pp. 47-56, Nov. 2011.
Paper # IBISML2011-49 
Date of Issue 2011-11-02 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IBISML2011-49

Conference Information
Committee IBISML  
Conference Date 2011-11-09 - 2011-11-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Nara Womens Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The 14th IBIS workshop 
Paper Information
Registration To IBISML 
Conference Code 2011-11-IBISML 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Kernel and Feature Search in Kernel PCA 
Sub Title (in English)  
Keyword(1) Unsupervised Learning  
Keyword(2) Kernel PCA  
Keyword(3) Pre-Iamges  
Keyword(4) Leave-one-out Cross-Validation  
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1st Author's Name Alam Md. Ashad  
1st Author's Affiliation The Graduate University for Advanced Studies (SOKENDAI)
2nd Author's Name Kenji Fukumizu  
2nd Author's Affiliation The Instituted of Statistical Mathematics (ISM)
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Speaker Author-1 
Date Time 2011-11-09 15:45:00 
Presentation Time 180 minutes 
Registration for IBISML 
Paper # IBISML2011-49 
Volume (vol) vol.111 
Number (no) no.275 
Page pp.47-56 
#Pages 10 
Date of Issue 2011-11-02 (IBISML) 

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