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Paper Abstract and Keywords
Presentation 2014-07-17 10:10
Distance Metric Learning with Low Computational Complexity based on Ensemble of Low-dimensional Matrixes
Hiroshi Saito, Fumihiro Yamazaki, Kenta Mikawa, Masayuki Goto (Waseda Univ.) IT2014-12
Abstract (in Japanese) (See Japanese page) 
(in English) The distance metric learning is the approach which enables to acquire a good metric for automatic data classification. In this paper, we focus on the typical distance metric learning approach of Mahalanobis Metric for Clustering (MMC). It is known that MMC has a problem such that the computational cost becomes enormous when the number of training data or dimension of the feature space becomes large. In order to solve this problem, we propose the combinational way based on ensemble of low-dimensional metric matrices produced by selecting several features randomly.
Keyword (in Japanese) (See Japanese page) 
(in English) distance metric learning / Mahalanobis Metric for Clustering / metric matrix / ensemble / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 138, IT2014-12, pp. 7-12, July 2014.
Paper # IT2014-12 
Date of Issue 2014-07-10 (IT) 
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 IT2014-12

Conference Information
Committee IT  
Conference Date 2014-07-17 - 2014-07-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Kobe University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) freshman session, general 
Paper Information
Registration To IT 
Conference Code 2014-07-IT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Distance Metric Learning with Low Computational Complexity based on Ensemble of Low-dimensional Matrixes 
Sub Title (in English)  
Keyword(1) distance metric learning  
Keyword(2) Mahalanobis Metric for Clustering  
Keyword(3) metric matrix  
Keyword(4) ensemble  
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1st Author's Name Hiroshi Saito  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Fumihiro Yamazaki  
2nd Author's Affiliation Waseda University (Waseda Univ.)
3rd Author's Name Kenta Mikawa  
3rd Author's Affiliation Waseda University (Waseda Univ.)
4th Author's Name Masayuki Goto  
4th Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2014-07-17 10:10:00 
Presentation Time 25 minutes 
Registration for IT 
Paper # IT2014-12 
Volume (vol) vol.114 
Number (no) no.138 
Page pp.7-12 
#Pages 6 
Date of Issue 2014-07-10 (IT) 

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