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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
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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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Conference Information
Committee MBE NC  
Conference Date 2013-03-13 - 2013-03-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Tamagawa University 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
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)  
Keyword(1) generative topographic mapping  
Keyword(2) visualization  
Keyword(3) Gaussian process  
Keyword(4) Markov chain Monte Carlo  
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1st Author's Name Nobuhiko Yamaguchi  
1st Author's Affiliation 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
Date of Issue 2013-03-06 (NC) 


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