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Paper Abstract and Keywords
Presentation 2016-07-06 13:00
A New Probabilistic Topic Model Based on Variable Bin Width Histogram
Hideaki Kim, Tomoharu Iwata, Hiroshi Sawada (NTT) IBISML2016-6
Abstract (in Japanese) (See Japanese page) 
(in English) Probabilistic topic models, as represented by latent Dirichlet allocation (LDA), have been widely used for analyzing not only categorical but also continuous data such as times of word appearance and price information. In the topic model for continuous data, however, the component distributions needs to be simple exponential families like normal distributions to perform the efficient parameter estimation, which limits the representative power of the model. In this paper, by incorporating the nonparametric histogram density estimator into the topic model, we construct a new probabilistic topic model to overcome the limitation. The estimation of the parameters, including the bin width selection, is performed by using efficient collapsed Gibbs sampling. We derive the estimation algorithms for the regular and variable bin width scenarios. We apply the proposed method to synthetic data, confirming that it performs well.
Keyword (in Japanese) (See Japanese page) 
(in English) LDA / topic model / histogram / bin width selection / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 121, IBISML2016-6, pp. 217-223, July 2016.
Paper # IBISML2016-6 
Date of Issue 2016-06-28 (IBISML) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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
Conference Date 2016-07-04 - 2016-07-06 
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 2016-07-NC-BIO-IBISML-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A New Probabilistic Topic Model Based on Variable Bin Width Histogram 
Sub Title (in English)  
Keyword(1) LDA  
Keyword(2) topic model  
Keyword(3) histogram  
Keyword(4) bin width selection  
1st Author's Name Hideaki Kim  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Tomoharu Iwata  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Hiroshi Sawada  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2016-07-06 13:00:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2016-6 
Volume (vol) vol.116 
Number (no) no.121 
Page pp.217-223 
Date of Issue 2016-06-28 (IBISML) 

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