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Paper Abstract and Keywords
Presentation 2015-06-23 11:10
Corpus and Topic Scalable Topic Model
Soma Yokoi, Issei Sato, Hiroshi Nakagawa (UTokyo) IBISML2015-5
Abstract (in Japanese) (See Japanese page) 
(in English) It is known that topic model with high dimensional topics improves IR performance like search engines and online advertisements, because it helps to model long-tail words in large scale corpora. However, high dimensional topics with large corpora cause 2 problems: computational performance and memory requirement. For the fundamental topic model, LDA, SGRLD LDA is proposed to scale to large corpora and AliasLDA to accelerate computing topics. In this paper, we propose a method for both topic computation and data scalability, by combining these techniques. Also careful calculation of gradients reduces required space to expectations. Experiments demonstrate that our method is scalable for both corpus size and topic dimension, also achieve faster runtime speed compared to the existing approach, especially 10+ times faster on high dimensional topics setting.
Keyword (in Japanese) (See Japanese page) 
(in English) topic modeling / Langevin MCMC / alias method / scalability / / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 112, IBISML2015-5, pp. 27-31, June 2015.
Paper # IBISML2015-5 
Date of Issue 2015-06-16 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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 NC IPSJ-BIO IBISML IPSJ-MPS  
Conference Date 2015-06-23 - 2015-06-25 
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 2015-06-NC-BIO-IBISML-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Corpus and Topic Scalable Topic Model 
Sub Title (in English)  
Keyword(1) topic modeling  
Keyword(2) Langevin MCMC  
Keyword(3) alias method  
Keyword(4) scalability  
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1st Author's Name Soma Yokoi  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Issei Sato  
2nd Author's Affiliation The University of Tokyo (UTokyo)
3rd Author's Name Hiroshi Nakagawa  
3rd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2015-06-23 11:10:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2015-5 
Volume (vol) vol.115 
Number (no) no.112 
Page pp.27-31 
#Pages
Date of Issue 2015-06-16 (IBISML) 


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