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 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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IBISML2016-1 |
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) |
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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 |
5 |
Date of Issue |
2016-06-28 (IBISML) |
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