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| Presentation | 2010-11-05 15:30 [Poster Presentation] Efficient Computation of Normalized Maximum Likelihood Coding for Gaussian Mixtures with Its Applications to Model Selection So Hirai, Kenji Yamanishi (Tokyo Univ.)  IBISML2010-103 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We are concerned with the issue of efficient computation of normalized maximum likelihood (NML) code-lengths for Gaussian mixture distributions with its applications to model selection.Although the NML code-length is the optimal one in that it achieves Shtarkov's minimax criterion,there is a problem that the normalization factor in the NML code-length becomes infinity in the case where data is continuous.In this paper we propose a method for computation of the NML code-length for Gaussian mixture distributions so that the normization term is bounded by a finite value and the total computation time is ${\rm O}(n^2)$($n$ is sample size).We empirically demonstrate the effectiveness of the proposed method in terms of the rate of convergence of the selected number of clusters in comparison with AIC and BIC. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Normalized Maximum Likelihood / Minimum Description Length Principle / Gaussian Mixture Distribution / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 110, no. 265, IBISML2010-103, pp. 327-333, Nov. 2010. | |
| Paper # | IBISML2010-103 | |
| Date of Issue | 2010-10-28 (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 | IBISML2010-103 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2010-11-04 - 2010-11-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | IIS, Univ. of Tokyo |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | IBIS 2010 (Workshop on Information-based Induction Sciences) |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2010-11-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Efficient Computation of Normalized Maximum Likelihood Coding for Gaussian Mixtures with Its Applications to Model Selection |
| Sub Title (in English) | |
| Keyword(1) | Normalized Maximum Likelihood |
| Keyword(2) | Minimum Description Length Principle |
| Keyword(3) | Gaussian Mixture Distribution |
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| 1st Author's Name | So Hirai |
| 1st Author's Affiliation | The University of Tokyo (Tokyo Univ.) |
| 2nd Author's Name | Kenji Yamanishi |
| 2nd Author's Affiliation | The University of Tokyo (Tokyo Univ.) |
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| Speaker | Author-1 |
| Date Time | 2010-11-05 15:30:00 |
| Presentation Time | 180 minutes |
| Registration for | IBISML |
| Paper # | IBISML2010-103 |
| Volume (vol) | vol.110 |
| Number (no) | no.265 |
| Page | pp.327-333 |
| #Pages | 7 |
| Date of Issue | 2010-10-28 (IBISML) |
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