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| Presentation | 2017-06-25 10:20 Learning with linearly transformed l0 sparsity Naoki Marumo, Tomoharu Iwata (NTT)  IBISML2017-8 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We consider a class of non-convex optimization problems with linearly transformed sparsity constraints, which includes a wide range of problems in machine learning. Since this problem is difficult to solve directly, $ell_1$ relaxation methods have been extensively used. However, $ell_1$ methods often suffer from large estimation bias. To avoid this problem, we propose a provable algorithm for solving the $ell_0$ problem without relaxation. Our analysis shows that the proposed method converges linearly to an approximately global optimum under mild assumptions. With our experiments, we confirm that the proposed method is faster and more accurate than existing $ell_1$ and $ell_0$ methods and that the proposed method can capture the true sparsity level more correctly. Furthermore, the results show that the proposed method works well under severe conditions such as small sample size and high dimension. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | nonconvex optimization / sparse optimization / machine learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 110, IBISML2017-8, pp. 193-199, June 2017. | |
| Paper # | IBISML2017-8 | |
| Date of Issue | 2017-06-17 (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) | |
| Download PDF | IBISML2017-8 |
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| Conference Information | |
| Committee | NC IPSJ-BIO IBISML IPSJ-MPS |
| Conference Date | 2017-06-23 - 2017-06-25 |
| 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 | 2017-06-NC-BIO-IBISML-MPS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Learning with linearly transformed l0 sparsity |
| Sub Title (in English) | |
| Keyword(1) | nonconvex optimization |
| Keyword(2) | sparse optimization |
| Keyword(3) | machine learning |
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| 1st Author's Name | Naoki Marumo |
| 1st Author's Affiliation | Nippon Telegraph and Telephone (NTT) |
| 2nd Author's Name | Tomoharu Iwata |
| 2nd Author's Affiliation | Nippon Telegraph and Telephone (NTT) |
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| Speaker | Author-1 |
| Date Time | 2017-06-25 10:20:00 |
| Presentation Time | 25 minutes |
| Registration for | IBISML |
| Paper # | IBISML2017-8 |
| Volume (vol) | vol.117 |
| Number (no) | no.110 |
| Page | pp.193-199 |
| #Pages | 7 |
| Date of Issue | 2017-06-17 (IBISML) |
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