Paper Abstract and Keywords |
Presentation |
2009-03-11 16:35
A Learning Algorithm of Helmholtz Machine with Mean Field Approximation Yuki Aoki (Nara Inst. of Sci and Tech.), Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NC2008-113 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
It is often required to extract a compact feature of an original high-dimensional datum. Such a compactfeature is useful to visualize the original data, or to avoid the over-fitting in the learning which usually requires parameters in proportion to the dimensionality of the data. Recently, Hinton and Salakhutdinov [8] proposed a new dimensionality reduction method, which shows a superior performance to the PCA in their demonstration. They used a hierarchical Boltzmann Machine with millions of parameters. It is well known that the learning of such a nonlinear function approximator is difficult because of local minima and overfitting; neverthless, it seems that they had avoided such possible problems by adopting a Contrastive Divergence (CD) learning in the pretraining. Although the CD learning provides a parameter update rule which realizes efficient learning, several potential disadvantages
remain: there are no theoretically justified choice for the step-size in updating the parameters because the cost function of CD learning is unknown.
In this report, we derive a learning algorithm of Helmholtz Machine
using a mean field approximation, which allows analytical evaluations of both of the cost function and its derivatives with respect to the parameters. Also, it is shown that the learning of the Helmholtz Machine is comparative to the CD learning of the Boltzmann machine under certain conditions. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Restricted Boltzmann Machine, / Helmholtz Machine, / Contrastive Divergence / Feature Extraction / Mean Field Approximation / / / |
Reference Info. |
IEICE Tech. Rep., vol. 108, no. 480, NC2008-113, pp. 57-62, March 2009. |
Paper # |
NC2008-113 |
Date of Issue |
2009-03-04 (NC) |
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) |
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NC2008-113 |
Conference Information |
Committee |
NC MBE |
Conference Date |
2009-03-11 - 2009-03-13 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Tamagawa Univ. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
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Paper Information |
Registration To |
NC |
Conference Code |
2009-03-NC-MBE |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Learning Algorithm of Helmholtz Machine with Mean Field Approximation |
Sub Title (in English) |
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Keyword(1) |
Restricted Boltzmann Machine, |
Keyword(2) |
Helmholtz Machine, |
Keyword(3) |
Contrastive Divergence |
Keyword(4) |
Feature Extraction |
Keyword(5) |
Mean Field Approximation |
Keyword(6) |
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Keyword(7) |
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Keyword(8) |
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1st Author's Name |
Yuki Aoki |
1st Author's Affiliation |
Nara Institute of Science and Technology (Nara Inst. of Sci and Tech.) |
2nd Author's Name |
Shin-ichi Maeda |
2nd Author's Affiliation |
Kyoto University (Kyoto Univ.) |
3rd Author's Name |
Shin Ishii |
3rd Author's Affiliation |
Kyoto University (Kyoto Univ.) |
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Speaker |
Author-1 |
Date Time |
2009-03-11 16:35:00 |
Presentation Time |
25 minutes |
Registration for |
NC |
Paper # |
NC2008-113 |
Volume (vol) |
vol.108 |
Number (no) |
no.480 |
Page |
pp.57-62 |
#Pages |
6 |
Date of Issue |
2009-03-04 (NC) |
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