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Paper Abstract and Keywords
Presentation 2007-12-13 15:30
Linear and Nonlinear Hybrid Kernel Mutual Subspace method for Object Recognition
Hitoshi Sakano (NTT Data) PRMU2007-143
Abstract (in Japanese) (See Japanese page) 
(in English) We propose new member of mutual subspace method (MSM) family named
linear and nonlinear hybrid kernel mutual subspace method (HKMS) for object recognition.
We already proposed kernelized nonlinear version of MSM named KMS.
Experimentally, effectiveness of KMS is demonstrated in object recognition problem.
However, from the theoretical view point, origine of effectiveness is unclear, because of properties of recognition data stream is differ from training data stream.
When we consider object recognition problem invariant against pose change, training data include various pose object images, and recognition data stream consist by similar images each other.
Therefore training data stream has strong nonlinear structure and recognition data stream has approximate linear structure.
KMS is not natural since KMS represent such data as same kernel parameter.
To solve this problem we propose HKMS, that consist of kernel principal component learning and linear-nonlinear hybrid recognition algorithms.
The experiment using artificial data show the effectiveness of HKMS.
Keyword (in Japanese) (See Japanese page) 
(in English) object recognition / subspace method / kernel PCA / mutual subspace method / kernel mutual subspace method / / /  
Reference Info. IEICE Tech. Rep., vol. 107, no. 384, PRMU2007-143, pp. 43-46, Dec. 2007.
Paper # PRMU2007-143 
Date of Issue 2007-12-06 (PRMU) 
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 PRMU2007-143

Conference Information
Committee PRMU  
Conference Date 2007-12-13 - 2007-12-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Kobe Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2007-12-PRMU 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Linear and Nonlinear Hybrid Kernel Mutual Subspace method for Object Recognition 
Sub Title (in English)  
Keyword(1) object recognition  
Keyword(2) subspace method  
Keyword(3) kernel PCA  
Keyword(4) mutual subspace method  
Keyword(5) kernel mutual subspace method  
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1st Author's Name Hitoshi Sakano  
1st Author's Affiliation NTT Data Corp. (NTT Data)
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Speaker Author-1 
Date Time 2007-12-13 15:30:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # PRMU2007-143 
Volume (vol) vol.107 
Number (no) no.384 
Page pp.43-46 
#Pages 4 
Date of Issue 2007-12-06 (PRMU) 

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