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Paper Abstract and Keywords
Presentation 2017-07-13 13:00
A Newton-Type Algorithm for Nonnegative Matrix Factorization with Alpha-Divergence
Satoshi Nakatsu, Norikazu Takahashi (Okayama Univ.) NLP2017-31
Abstract (in Japanese) (See Japanese page) 
(in English) Nonnegative Matrix Factorization (NMF) is an operation that decomposes a given nonnegative matrix into two nonnegative factor matrices. NMF is formulated as a constrained optimization problem that minimizes an error function under the constraint that all variables are nonnegative. In this report we propose a novel iterative algorithm for nonnegative matrix factorization with the alpha-divergence. The proposed algorithm is based on the coordinate descent and the Newton method. We show that the proposed algorithm has the global convergence property in the sense that the sequence of solutions has at least one convergent subsequence and the limit of any convergent subsequence is a stationary point of the corresponding optimization problem. We also show through numerical experiments that the proposed algorithm is much faster than the multiplicative update rule.
Keyword (in Japanese) (See Japanese page) 
(in English) nonnegative matrix factorization / alpha-divergence / optimization problem / Newton's method / global convergence / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 121, NLP2017-31, pp. 17-22, July 2017.
Paper # NLP2017-31 
Date of Issue 2017-07-06 (NLP) 
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)
Download PDF NLP2017-31

Conference Information
Committee NLP  
Conference Date 2017-07-13 - 2017-07-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Miyako Island Marine Terminal 
Topics (in Japanese) (See Japanese page) 
Topics (in English) etc. 
Paper Information
Registration To NLP 
Conference Code 2017-07-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Newton-Type Algorithm for Nonnegative Matrix Factorization with Alpha-Divergence 
Sub Title (in English)  
Keyword(1) nonnegative matrix factorization  
Keyword(2) alpha-divergence  
Keyword(3) optimization problem  
Keyword(4) Newton's method  
Keyword(5) global convergence  
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Keyword(8)  
1st Author's Name Satoshi Nakatsu  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Norikazu Takahashi  
2nd Author's Affiliation Okayama University (Okayama Univ.)
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Speaker Author-1 
Date Time 2017-07-13 13:00:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2017-31 
Volume (vol) vol.117 
Number (no) no.121 
Page pp.17-22 
#Pages 6 
Date of Issue 2017-07-06 (NLP) 

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