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| Presentation | 2014-11-18 15:00 [Poster Presentation] Optimization Method of LASSO Hyperparameter using WAIC Dai Miyazaki, Sumio Watanabe (Tokyo Tech)  IBISML2014-63 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | LASSO (Least Absolute Shrinkage and Selection Operator) was proposed as a regression method using a penalty term made of the sum of absolute values of parameters, which is expected to be effective when the true parameter is sparse. However, it is not yet established how to optimize its hyperparameter. In this paper, we study LASSO from the Bayesian point of view, and propose an optimization method of the hyperparameter using the information criterion WAIC which is an asymptotic unbiased estimator of the generalization error. Experimentally, we show four points. (1) By using WAIC, the average generalization error of LASSO can be estimated. (2) If the true parameter is sparse, then LASSO is effective and the optimal hyperparameter can be found by WAIC. (3) If the true parameter is not sparse, then LASSO is not effective which can be observed by using WAIC. (4) In, LASSO, the hyperparameter which minimizes the marginal likelihood is different from the hyperparameter which minimizes WAIC and the average generalization error. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | LASSO regression / Hyperparameter / WAIC / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 114, no. 306, IBISML2014-63, pp. 213-218, Nov. 2014. | |
| Paper # | IBISML2014-63 | |
| Date of Issue | 2014-11-10 (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 | IBISML2014-63 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2014-11-17 - 2014-11-19 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nagoya Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2014-11-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Optimization Method of LASSO Hyperparameter using WAIC |
| Sub Title (in English) | |
| Keyword(1) | LASSO regression |
| Keyword(2) | Hyperparameter |
| Keyword(3) | WAIC |
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| 1st Author's Name | Dai Miyazaki |
| 1st Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 2nd Author's Name | Sumio Watanabe |
| 2nd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
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| Speaker | Author-1 |
| Date Time | 2014-11-18 15:00:00 |
| Presentation Time | 180 minutes |
| Registration for | IBISML |
| Paper # | IBISML2014-63 |
| Volume (vol) | vol.114 |
| Number (no) | no.306 |
| Page | pp.213-218 |
| #Pages | 6 |
| Date of Issue | 2014-11-10 (IBISML) |
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