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| Paper Abstract and Keywords | ||
| Presentation | 2020-01-09 13:50 Dimensionality reduction method for gaussian process posteriors based on information geometry Hideaki Ishibashi (Kyutech), Shotaro Akaho (AIST/RIKEN)  IBISML2019-20 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This paper proposes an extension of principal component analysis for gaussian process posteriors which is denoted by GP-PCA. GP-PCA can be applied to multi-task learning, meta-learning and transfer learning. The issue is how to define an structure of a set of GP posteriors such as a coordinate system and a distance. In this study, we define infinite dimensional structure reduced to finite dimensional structure based on information geometry. Especially, we show that a set of GP posteriors becomes a finite dimensional dually flat. Moreover, we demonstrate the effectiveness of GP-PCA through experiments. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | information geometry / gaussian process / multi-task learning / meta-learning / transfer learning / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 360, IBISML2019-20, pp. 17-24, Jan. 2020. | |
| Paper # | IBISML2019-20 | |
| Date of Issue | 2020-01-02 (IBISML) | |
| ISSN | 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 | IBISML2019-20 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2020-01-09 - 2020-01-10 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | ISM |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Machine learning, etc. |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2020-01-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Dimensionality reduction method for gaussian process posteriors based on information geometry |
| Sub Title (in English) | |
| Keyword(1) | information geometry |
| Keyword(2) | gaussian process |
| Keyword(3) | multi-task learning |
| Keyword(4) | meta-learning |
| Keyword(5) | transfer learning |
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| Keyword(8) | |
| 1st Author's Name | Hideaki Ishibashi |
| 1st Author's Affiliation | Kyushu Institute of Technology (Kyutech) |
| 2nd Author's Name | Shotaro Akaho |
| 2nd Author's Affiliation | National Institute of Advanced Industrial Science and Technology/RIKEN (AIST/RIKEN) |
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| Speaker | Author-1 |
| Date Time | 2020-01-09 13:50:00 |
| Presentation Time | 25 minutes |
| Registration for | IBISML |
| Paper # | IBISML2019-20 |
| Volume (vol) | vol.119 |
| Number (no) | no.360 |
| Page | pp.17-24 |
| #Pages | 8 |
| Date of Issue | 2020-01-02 (IBISML) |
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