Paper Abstract and Keywords |
Presentation |
2013-03-15 10:15
Bayesian inference for GTM using non-stationary Gaussian process Nobuhiko Yamaguchi (Saga Univ.) NC2012-168 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Generative Topographic Mapping (GTM) is a nonlinear topographically preserving mapping from latent to data space introduced by Bishop et al. as a data visualization technique. The GTM can be interpreted as a probabilistic model with Gaussian process prior, whose properties depend on the covariance function of the Gaussian process. The conventional GTM approaches use a covariance function with a constant lengthscale, and therefore fail to adapt to variable smoothness of the nonlinear topographically preserving mapping. In this paper, we propose the GTM that can individually control the smoothness in each local region of the latent space. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
generative topographic mapping / visualization / Gaussian process / Markov chain Monte Carlo / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 112, no. 480, NC2012-168, pp. 197-202, March 2013. |
Paper # |
NC2012-168 |
Date of Issue |
2013-03-06 (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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NC2012-168 |
Conference Information |
Committee |
MBE NC |
Conference Date |
2013-03-13 - 2013-03-15 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Tamagawa University |
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(See Japanese page) |
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Paper Information |
Registration To |
NC |
Conference Code |
2013-03-MBE-NC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Bayesian inference for GTM using non-stationary Gaussian process |
Sub Title (in English) |
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generative topographic mapping |
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visualization |
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Gaussian process |
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Markov chain Monte Carlo |
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1st Author's Name |
Nobuhiko Yamaguchi |
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Saga University (Saga Univ.) |
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Speaker |
Author-1 |
Date Time |
2013-03-15 10:15:00 |
Presentation Time |
25 minutes |
Registration for |
NC |
Paper # |
NC2012-168 |
Volume (vol) |
vol.112 |
Number (no) |
no.480 |
Page |
pp.197-202 |
#Pages |
6 |
Date of Issue |
2013-03-06 (NC) |