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Paper Abstract and Keywords
Presentation 2016-07-05 13:00
Non-linear Embedded Feature Extraction Method using Comb-shaped Neural Network
Akihito Sudo (UT), Tomoyuki Higuchi, Shin'ya Nakano, Masaya Saito (ISM), Takahiro Yabe, Yoshihide Sekimoto (UT) IBISML2016-1
Abstract (in Japanese) (See Japanese page) 
(in English) Feature selection methods can be divided into three categories; wrapper methods, filter methods, and embedded methods. Embedded methods recently attract many researchers due to the effectiveness for the sparse modeling. One of important issues in researches of the embedded methods are devising non-linear methods, and methods employing non-linear kernel has been proposed. In the present paper, we propose a non-linear embedded feature selection method employing deep neural network. In the experiment using two synthetic datasets, the proposed method outperforms Lasso in terms of both feature selection and function approximation.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Learning / Sparse Modeling / Feature Selection / Lasso / Nonlinear / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 121, IBISML2016-1, pp. 127-131, July 2016.
Paper # IBISML2016-1 
Date of Issue 2016-06-28 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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 NC IPSJ-BIO IBISML IPSJ-MPS  
Conference Date 2016-07-04 - 2016-07-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning Approach to Biodata Mining, and General 
Paper Information
Registration To IBISML 
Conference Code 2016-07-NC-BIO-IBISML-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Non-linear Embedded Feature Extraction Method using Comb-shaped Neural Network 
Sub Title (in English)  
Keyword(1) Deep Learning  
Keyword(2) Sparse Modeling  
Keyword(3) Feature Selection  
Keyword(4) Lasso  
Keyword(5) Nonlinear  
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1st Author's Name Akihito Sudo  
1st Author's Affiliation the University of Tokyo (UT)
2nd Author's Name Tomoyuki Higuchi  
2nd Author's Affiliation the Institute of Statistical Mathematics (ISM)
3rd Author's Name Shin'ya Nakano  
3rd Author's Affiliation the Institute of Statistical Mathematics (ISM)
4th Author's Name Masaya Saito  
4th Author's Affiliation the Institute of Statistical Mathematics (ISM)
5th Author's Name Takahiro Yabe  
5th Author's Affiliation the University of Tokyo (UT)
6th Author's Name Yoshihide Sekimoto  
6th Author's Affiliation the University of Tokyo (UT)
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Speaker Author-1 
Date Time 2016-07-05 13:00:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2016-1 
Volume (vol) vol.116 
Number (no) no.121 
Page pp.127-131 
#Pages
Date of Issue 2016-06-28 (IBISML) 


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