Multi-agent solver for non-negative matrix factorization based on optimization

Zhipeng Tu; Weijian Li

Kybernetika (2021)

  • Issue: 1, page 60-77
  • ISSN: 0023-5954

Abstract

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This paper investigates a distributed solver for non-negative matrix factorization (NMF) over a multi-agent network. After reformulating the problem into the standard distributed optimization form, we design our distributed algorithm (DisNMF) based on the primal-dual method and in the form of multiplicative update rule. With the help of auxiliary functions, we provide monotonic convergence analysis. Furthermore, we show by computational complexity analysis and numerical examples that our distributed NMF algorithm performs well in comparison with the centralized NMF algorithm.

How to cite

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Tu, Zhipeng, and Li, Weijian. "Multi-agent solver for non-negative matrix factorization based on optimization." Kybernetika (2021): 60-77. <http://eudml.org/doc/297550>.

@article{Tu2021,
abstract = {This paper investigates a distributed solver for non-negative matrix factorization (NMF) over a multi-agent network. After reformulating the problem into the standard distributed optimization form, we design our distributed algorithm (DisNMF) based on the primal-dual method and in the form of multiplicative update rule. With the help of auxiliary functions, we provide monotonic convergence analysis. Furthermore, we show by computational complexity analysis and numerical examples that our distributed NMF algorithm performs well in comparison with the centralized NMF algorithm.},
author = {Tu, Zhipeng, Li, Weijian},
journal = {Kybernetika},
keywords = {distributed optimization; non-negative matrix factorization; multiplicative update rules; multi-agent network},
language = {eng},
number = {1},
pages = {60-77},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Multi-agent solver for non-negative matrix factorization based on optimization},
url = {http://eudml.org/doc/297550},
year = {2021},
}

TY - JOUR
AU - Tu, Zhipeng
AU - Li, Weijian
TI - Multi-agent solver for non-negative matrix factorization based on optimization
JO - Kybernetika
PY - 2021
PB - Institute of Information Theory and Automation AS CR
IS - 1
SP - 60
EP - 77
AB - This paper investigates a distributed solver for non-negative matrix factorization (NMF) over a multi-agent network. After reformulating the problem into the standard distributed optimization form, we design our distributed algorithm (DisNMF) based on the primal-dual method and in the form of multiplicative update rule. With the help of auxiliary functions, we provide monotonic convergence analysis. Furthermore, we show by computational complexity analysis and numerical examples that our distributed NMF algorithm performs well in comparison with the centralized NMF algorithm.
LA - eng
KW - distributed optimization; non-negative matrix factorization; multiplicative update rules; multi-agent network
UR - http://eudml.org/doc/297550
ER -

References

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