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| Presentation | 2013-01-24 14:45 Vector output estimation by Gaussian Process Regression using dynamic Active Set Yuki Matsumura, Toshikazu Wada (Wakayama Univ.), Shunji Maeda, Hisae Shibuya (Hitachi)  PRMU2012-120 MVE2012-85 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This report presents a method to estimate vector outputs in the framework of Gaussian Process Regression (GPR) . GPR is a non-linear regression method based on input-output examples. Since basic GPR estimates an output consisting of scalar mean and variance, multiple executions of GPR cannot estimate covariance between vector components. Our method estimates both vector mean values and covariance matrices based on our previous method Dynamic Active Set (DAS). Active set is the set of examples consisting of input and output pairs. DAS automatically construct an active set suitable for estimating the output for given input. Our method estimates covariance matrix from the output components of the Active Set obtained by DAS. Experimental results for artificial and real datasets demonstrate the soundness of our method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Gaussian Process Regression / Example based non-linear regression / Dynamic Active Set / covariance matrix estimation / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 112, no. 385, PRMU2012-120, pp. 317-322, Jan. 2013. | |
| Paper # | PRMU2012-120 | |
| Date of Issue | 2013-01-16 (PRMU, MVE) | |
| 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 | PRMU2012-120 MVE2012-85 |
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| Conference Information | |
| Committee | PRMU MVE IPSJ-CVIM |
| Conference Date | 2013-01-23 - 2013-01-24 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2013-01-PRMU-MVE-CVIM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Vector output estimation by Gaussian Process Regression using dynamic Active Set |
| Sub Title (in English) | |
| Keyword(1) | Gaussian Process Regression |
| Keyword(2) | Example based non-linear regression |
| Keyword(3) | Dynamic Active Set |
| Keyword(4) | covariance matrix estimation |
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| 1st Author's Name | Yuki Matsumura |
| 1st Author's Affiliation | Wakayama University (Wakayama Univ.) |
| 2nd Author's Name | Toshikazu Wada |
| 2nd Author's Affiliation | Wakayama University (Wakayama Univ.) |
| 3rd Author's Name | Shunji Maeda |
| 3rd Author's Affiliation | Hitachi, Ltd. (Hitachi) |
| 4th Author's Name | Hisae Shibuya |
| 4th Author's Affiliation | Hitachi, Ltd. (Hitachi) |
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| Speaker | Author-1 |
| Date Time | 2013-01-24 14:45:00 |
| Presentation Time | 30 minutes |
| Registration for | PRMU |
| Paper # | PRMU2012-120, MVE2012-85 |
| Volume (vol) | vol.112 |
| Number (no) | no.385(PRMU), no.386(MVE) |
| Page | pp.317-322 |
| #Pages | 6 |
| Date of Issue | 2013-01-16 (PRMU, MVE) |
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