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| Presentation | 2017-06-25 09:30 Expectation Propagation for t-Exponential Family Futoshi Futami, Issei Sato (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo)  IBISML2017-6 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Exponential family distributions are highly useful in machine learning since their calculation can be performed efficiently through natural parameters. The exponential family has recently been extended to the t-exponential family, which contains Student-t distributions as family members and thus allows us to handle noisy data well. However, since the t-exponential family is defined by the deformed exponential, we cannot derive an efficient learning algorithm for the t-exponential family such as expectation propagation (EP). In this paper, we borrow the mathematical tools of q-algebra from statistical physics and show that the pseudo additivity of distributions allows us to perform calculation of t-exponential family distributions through natural parameters. We then develop an expectation propagation (EP) algorithm for the t-exponential family, which provides a deterministic approximation to the posterior or predictive distribution with simple moment matching. We finally apply the proposed EP algorithm to the Bayes point machine and Student-t process classification, and demonstrate their performance numerically. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | exponential family / Student-t / Gaussian / pseudo additivity / expectation propagation / assumed density filter / gaussian process / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 110, IBISML2017-6, pp. 179-184, June 2017. | |
| Paper # | IBISML2017-6 | |
| 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 | IBISML2017-6 |
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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 | English (Japanese title is available) |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Expectation Propagation for t-Exponential Family |
| Sub Title (in English) | |
| Keyword(1) | exponential family |
| Keyword(2) | Student-t |
| Keyword(3) | Gaussian |
| Keyword(4) | pseudo additivity |
| Keyword(5) | expectation propagation |
| Keyword(6) | assumed density filter |
| Keyword(7) | gaussian process |
| Keyword(8) | |
| 1st Author's Name | Futoshi Futami |
| 1st Author's Affiliation | The University of Tokyo/RIKEN (Univ. of Tokyo/RIKEN) |
| 2nd Author's Name | Issei Sato |
| 2nd Author's Affiliation | The University of Tokyo/RIKEN (Univ. of Tokyo/RIKEN) |
| 3rd Author's Name | Masashi Sugiyama |
| 3rd Author's Affiliation | RIKEN/The University of Tokyo (RIKEN/Univ. of Tokyo) |
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| Speaker | 1 |
| Date Time | 2017-06-25 09:30:00 |
| Presentation Time | 25 |
| Registration for | IBISML |
| Paper # | IEICE-IBISML2017-6 |
| Volume (vol) | IEICE-117 |
| Number (no) | no.110 |
| Page | pp.179-184 |
| #Pages | IEICE-6 |
| Date of Issue | IEICE-IBISML-2017-06-17 |
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