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Paper Abstract and Keywords
Presentation 2014-11-18 15:00
[Poster Presentation] Unsupervised Dimension Reduction via Least-Squares Quadratic Mutual Information
Janya Sainui (Tokyo Inst. of Tech.), Masashi Sugiyama (Univ. of Tokyo) IBISML2014-69
Abstract (in Japanese) (See Japanese page) 
(in English) The goal of dimension reduction is to represent high-dimensional data in a lower-dimensional subspace, while intrinsic properties of the original data are kept as much as possible. An important challenge in unsupervised dimension reduction is the choice of tuning parameters, because no supervised information is available and thus parameter selection tends to be subjective and heuristic. In this paper, we propose an information-theoretic approach to unsupervised dimension reduction that allows objective tuning parameter selection. We employ quadratic mutual information (QMI) as our information measure, which is known to be less sensitive to outliers than ordinary mutual information, and QMI is estimated analytically by a least-squares method in a computationally efficient way. Then, we provide an eigenvector-based efficient implementation for performing unsupervised dimension reduction based on the QMI estimator. The usefulness of the proposed method is demonstrated through experiments.
Keyword (in Japanese) (See Japanese page) 
(in English) unsupervised dimension reduction / quadratic mutual information / least-squares density difference / Epanechnikov kernel / hyperparameter tuning / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 306, IBISML2014-69, pp. 259-262, Nov. 2014.
Paper # IBISML2014-69 
Date of Issue 2014-11-10 (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)
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Conference Information
Committee IBISML  
Conference Date 2014-11-17 - 2014-11-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Nagoya Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IBISML 
Conference Code 2014-11-IBISML 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Unsupervised Dimension Reduction via Least-Squares Quadratic Mutual Information 
Sub Title (in English)  
Keyword(1) unsupervised dimension reduction  
Keyword(2) quadratic mutual information  
Keyword(3) least-squares density difference  
Keyword(4) Epanechnikov kernel  
Keyword(5) hyperparameter tuning  
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Keyword(7)  
Keyword(8)  
1st Author's Name Janya Sainui  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Inst. of Tech.)
2nd Author's Name Masashi Sugiyama  
2nd Author's Affiliation The University of Tokyo (Univ. of Tokyo)
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Speaker Author-1 
Date Time 2014-11-18 15:00:00 
Presentation Time 180 minutes 
Registration for IBISML 
Paper # IBISML2014-69 
Volume (vol) vol.114 
Number (no) no.306 
Page pp.259-262 
#Pages 4 
Date of Issue 2014-11-10 (IBISML) 

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