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Paper Abstract and Keywords
Presentation 2023-03-17 14:50
Reformulation of Optimization Problem in Randomized NMF and Proposal of A Novel Iterative Update Algorithm
Takao Masuda, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) MSS2022-105 NLP2022-150
Abstract (in Japanese) (See Japanese page) 
(in English) As an approach to efficiently perform large-scale Nonnegative Matrix Factorization (NMF), a randomized NMF was recently proposed. This approach reduces the dimension of a given nonnegative matrix by multiplying it by a random matrix, and then performs matrix factorization. However, algorithms for standard NMF cannot be directly used for randomized NMF because linear inequality constraints have to be considered instead of nonnegativity constraints. Although an algorithm for randomized NMF has been proposed, it has a serious drawback that the constraints are not always satisfied. In this report, we reformulate the optimization problem of randomized NMF from a different perspective and propose a novel iterative update algorithm that combines the hierarchical alternating least squares method and projection onto the feasible region. We also prove that the proposed algorithm has global convergence property.
Keyword (in Japanese) (See Japanese page) 
(in English) nonnegative matrix factorization / randomized NMF / hierarchical alternating least squares algorithm / global convergence / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 436, NLP2022-150, pp. 204-209, March 2023.
Paper # NLP2022-150 
Date of Issue 2023-03-08 (MSS, NLP) 
ISSN 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 MSS2022-105 NLP2022-150

Conference Information
Committee NLP MSS  
Conference Date 2023-03-15 - 2023-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NLP 
Conference Code 2023-03-NLP-MSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Reformulation of Optimization Problem in Randomized NMF and Proposal of A Novel Iterative Update Algorithm 
Sub Title (in English)  
Keyword(1) nonnegative matrix factorization  
Keyword(2) randomized NMF  
Keyword(3) hierarchical alternating least squares algorithm  
Keyword(4) global convergence  
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1st Author's Name Takao Masuda  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Tsuyoshi Migita  
2nd Author's Affiliation Okayama University (Okayama Univ.)
3rd Author's Name Norikazu Takahashi  
3rd Author's Affiliation Okayama University (Okayama Univ.)
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Speaker Author-1 
Date Time 2023-03-17 14:50:00 
Presentation Time 20 minutes 
Registration for NLP 
Paper # MSS2022-105, NLP2022-150 
Volume (vol) vol.122 
Number (no) no.435(MSS), no.436(NLP) 
Page pp.204-209 
#Pages 6 
Date of Issue 2023-03-08 (MSS, NLP) 

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