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Paper Abstract and Keywords
Presentation 2016-11-17 14:00
Exhaustive search for sparse variable selection in linear regression
Yasuhiko Igarashi, Hikaru Takenaka, Nakanishi-Ohno Yoshinori (UTokyo), Makoto Uemura (Hiroshima Univ.), Shiro Ikeda (ISM), Masato Okada (UTokyo) IBISML2016-90
Abstract (in Japanese) (See Japanese page) 
(in English) We proposed the $K$-sparse
Exhaustive-Search (ES-$K$) method,
in which, assuming the optimum combination of
explanatory variables is $K$-sparse,
we exhaustively search the $K$-sparse combinations for sparse variable selection in linear regression.
We then obtain the density of states and can map to it solutions obtained by various approximate methods of sparse variable selection.
This method enables us to integrate the previous sparse variable selection methods
such as relaxation approach and sampling approach,
and evaluate all the approximate methods.
In addition, for the problem of combinatorial explosion of the explanatory variables,
we effectively reconstructed the density of states
by using the exchange Monte Carlo method and multi-histogram method.
Finally, we applied the ES-$K$ method to the type Ia supernova data.
As a result, there is a combination of explanatory variables
with higher performance than that of previous approximate sparse variable selection methods.
This result means that relaxation approach for sparse variable selection used in previous studies
is incomplete.
Keyword (in Japanese) (See Japanese page) 
(in English) $K$-sparse Exhaustive-Search (ES-$K$) method / BIC / CVE / LASSO / exchange Monte Carlo method / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 300, IBISML2016-90, pp. 313-320, Nov. 2016.
Paper # IBISML2016-90 
Date of Issue 2016-11-09 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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 2016-11-16 - 2016-11-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information-Based Induction Science Workshop (IBIS2016) 
Paper Information
Registration To IBISML 
Conference Code 2016-11-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Exhaustive search for sparse variable selection in linear regression 
Sub Title (in English)  
Keyword(1) $K$-sparse Exhaustive-Search (ES-$K$) method  
Keyword(2) BIC  
Keyword(3) CVE  
Keyword(4) LASSO  
Keyword(5) exchange Monte Carlo method  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Yasuhiko Igarashi  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Hikaru Takenaka  
2nd Author's Affiliation The University of Tokyo (UTokyo)
3rd Author's Name Nakanishi-Ohno Yoshinori  
3rd Author's Affiliation The University of Tokyo (UTokyo)
4th Author's Name Makoto Uemura  
4th Author's Affiliation Hiroshima University (Hiroshima Univ.)
5th Author's Name Shiro Ikeda  
5th Author's Affiliation The Institute of Statistical Mathematics (ISM)
6th Author's Name Masato Okada  
6th Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2016-11-17 14:00:00 
Presentation Time 180 minutes 
Registration for IBISML 
Paper # IBISML2016-90 
Volume (vol) vol.116 
Number (no) no.300 
Page pp.313-320 
#Pages
Date of Issue 2016-11-09 (IBISML) 


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