A Distributed HALS Algorithm for Euclidean Distance-Based Nonnegative Matrix Factorization

Yohei Domen; Tsuyoshi Migita; Norikazu Takahashi

doi:10.1109/SSCI44817.2019.9003158

A Distributed HALS Algorithm for Euclidean Distance-Based Nonnegative Matrix Factorization

Yohei Domen, Tsuyoshi Migita, Norikazu Takahashi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

This paper proposes a distributed algorithm for multiple agents to perform the Nonnegative Matrix Factorization (NMF) based on the Euclidean distance. The matrix to be factorized is partitioned into multiple blocks, and each block is assigned to one of the agents forming a two-dimensional grid network. Each agent handles a small number of entries of the factor matrices corresponding to the assigned block, and updates their values by using information coming from the neighbors. It is shown that the proposed algorithm simulates the hierarchical alternating least squares method, which is well known as a fast algorithm for NMF based on the Euclidean distance, by making use of a finite-time distributed consensus algorithm.

Original language	English
Title of host publication	2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1332-1337
Number of pages	6
ISBN (Electronic)	9781728124858
DOIs	https://doi.org/10.1109/SSCI44817.2019.9003158
Publication status	Published - Dec 2019
Event	2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, China Duration: Dec 6 2019 → Dec 9 2019

Publication series

Name	2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

Conference

Conference	2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Country/Territory	China
City	Xiamen
Period	12/6/19 → 12/9/19

Keywords

distributed algorithm
finite-time consensus
hierarchical alternating least squares method
multiagent network
nonnegative matrix factorization

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Modelling and Simulation

Access to Document

10.1109/SSCI44817.2019.9003158

Cite this

Domen, Y., Migita, T., & Takahashi, N. (2019). A Distributed HALS Algorithm for Euclidean Distance-Based Nonnegative Matrix Factorization. In 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (pp. 1332-1337). [9003158] (2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI44817.2019.9003158

A Distributed HALS Algorithm for Euclidean Distance-Based Nonnegative Matrix Factorization. / Domen, Yohei; Migita, Tsuyoshi ; Takahashi, Norikazu.

2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1332-1337 9003158 (2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Domen, Y, Migita, T & Takahashi, N 2019, A Distributed HALS Algorithm for Euclidean Distance-Based Nonnegative Matrix Factorization. in 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019., 9003158, 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1332-1337, 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019, Xiamen, China, 12/6/19. https://doi.org/10.1109/SSCI44817.2019.9003158

Domen Y, Migita T , Takahashi N. A Distributed HALS Algorithm for Euclidean Distance-Based Nonnegative Matrix Factorization. In 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1332-1337. 9003158. (2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019). doi: 10.1109/SSCI44817.2019.9003158

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abstract = "This paper proposes a distributed algorithm for multiple agents to perform the Nonnegative Matrix Factorization (NMF) based on the Euclidean distance. The matrix to be factorized is partitioned into multiple blocks, and each block is assigned to one of the agents forming a two-dimensional grid network. Each agent handles a small number of entries of the factor matrices corresponding to the assigned block, and updates their values by using information coming from the neighbors. It is shown that the proposed algorithm simulates the hierarchical alternating least squares method, which is well known as a fast algorithm for NMF based on the Euclidean distance, by making use of a finite-time distributed consensus algorithm.",

keywords = "distributed algorithm, finite-time consensus, hierarchical alternating least squares method, multiagent network, nonnegative matrix factorization",

author = "Yohei Domen and Tsuyoshi Migita and Norikazu Takahashi",

note = "Funding Information: This work was supported in part by Okawa Foundation for Information and Telecommunications. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 ; Conference date: 06-12-2019 Through 09-12-2019",

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A Distributed HALS Algorithm for Euclidean Distance-Based Nonnegative Matrix Factorization

Abstract

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Keywords

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