Paper Abstract and Keywords |
Presentation |
2023-03-17 14:30
Global Convergence Analysis of Distributed HALS Algorithm for Nonnegative Matrix Factorization Keiju Hayashi, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) MSS2022-104 NLP2022-149 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
As a fast computational method for Nonnegative Matrix Factorization (NMF),
the Hierarchical Alternating Least Squares (HALS) algorithm is widely known.
Recently, an algorithm for multiple agents communicating with each other to execute the HALS algorithm in a distributed manner was proposed. This algorithm enables us to perform NMF on large matrices that cannot be handled by a single agent. However, the average consensus algorithm used there requires each agent to store the entire history of the values of its variables until the complete average consensus is reached. This increases the memory usage and computational cost of each agent. In order to solve this problem, the authors proposed a distributed HALS algorithm using a simpler average consensus algorithm in which each agent does not have to store the entire history of the values. In this report, we prove that the proposed algorithm has the Zangwill's global convergence property if the number of iterations of the average consensus algorithm is properly set. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
nonnegative matrix factorization / hierarchical alternating least squares algorithm / distributed algorithm / average consensus / global convergence / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 436, NLP2022-149, pp. 198-203, March 2023. |
Paper # |
NLP2022-149 |
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) |
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MSS2022-104 NLP2022-149 |
Conference Information |
Committee |
NLP MSS |
Conference Date |
2023-03-15 - 2023-03-17 |
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(See Japanese page) |
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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) |
Global Convergence Analysis of Distributed HALS Algorithm for Nonnegative Matrix Factorization |
Sub Title (in English) |
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Keyword(1) |
nonnegative matrix factorization |
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hierarchical alternating least squares algorithm |
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distributed algorithm |
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average consensus |
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global convergence |
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1st Author's Name |
Keiju Hayashi |
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 |
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Okayama University (Okayama Univ.) |
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Speaker |
Author-1 |
Date Time |
2023-03-17 14:30:00 |
Presentation Time |
20 minutes |
Registration for |
NLP |
Paper # |
MSS2022-104, NLP2022-149 |
Volume (vol) |
vol.122 |
Number (no) |
no.435(MSS), no.436(NLP) |
Page |
pp.198-203 |
#Pages |
6 |
Date of Issue |
2023-03-08 (MSS, NLP) |
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