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Paper Abstract and Keywords
Presentation 2016-09-05 15:45
Sparse learning for pattern mining problem by using Safe Pattern Pruning method
Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama (NIT), Koji Tsuda (Univ. of Tokyo), Ichiro Takeuchi (NIT) PRMU2016-70 IBISML2016-25
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a subset of predictive patterns in the database. Our main contribution is to introduce a novel method called safe pattern pruning (SPP) for a class of predictive pattern mining problems. The SPP method allows us to efficiently find a superset of all the predictive patterns in the database that are needed for the optimal predictive model. The advantage of the SPP method over existing boosting-type method is that the former can find the superset by a single search over the database, while the latter requires multiple searches. The SPP method is inspired by recent development of safe feature screening. In order to extend the idea of safe feature screening into predictive pattern mining, we derive a novel pruning rule called safe pattern pruning (SPP) rule that can be used for searching over the tree defined among patterns in the database. The SPP rule has a property that,if a node corresponding to a pattern in the database is pruned out by the SPP rule,then it is guaranteed that all the patterns corresponding to its descendant nodes are never needed for the optimal predictive model. We apply the SPP method to graph mining and item-set mining problems, and demonstrate its computational advantage.
Keyword (in Japanese) (See Japanese page) 
(in English) Pattern mining / Sparse learning / Safe screening / Convex optimization / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 209, IBISML2016-25, pp. 127-134, Sept. 2016.
Paper # IBISML2016-25 
Date of Issue 2016-08-29 (PRMU, IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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 PRMU IPSJ-CVIM IBISML  
Conference Date 2016-09-05 - 2016-09-06 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Topics (in English)  
Paper Information
Registration To IBISML 
Conference Code 2016-09-PRMU-CVIM-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Sparse learning for pattern mining problem by using Safe Pattern Pruning method 
Sub Title (in English)  
Keyword(1) Pattern mining  
Keyword(2) Sparse learning  
Keyword(3) Safe screening  
Keyword(4) Convex optimization  
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1st Author's Name Kazuya Nakagawa  
1st Author's Affiliation Nagoya Institute of Technology (NIT)
2nd Author's Name Shinya Suzumura  
2nd Author's Affiliation Nagoya Institute of Technology (NIT)
3rd Author's Name Masayuki Karasuyama  
3rd Author's Affiliation Nagoya Institute of Technology (NIT)
4th Author's Name Koji Tsuda  
4th Author's Affiliation University of Tokyo (Univ. of Tokyo)
5th Author's Name Ichiro Takeuchi  
5th Author's Affiliation Nagoya Institute of Technology (NIT)
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Speaker Author-1 
Date Time 2016-09-05 15:45:00 
Presentation Time 30 minutes 
Registration for IBISML 
Paper # PRMU2016-70, IBISML2016-25 
Volume (vol) vol.116 
Number (no) no.208(PRMU), no.209(IBISML) 
Page pp.127-134 
#Pages
Date of Issue 2016-08-29 (PRMU, IBISML) 


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