Accelerated stochastic multiplicative update with gradient averaging for nonnegative matrix factorizations

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nonnegative matrix factorization (NMF) is a powerful tool in data analysis by discovering latent features and part-based patterns from high-dimensional data, and is a special case in which factor matrices have low-rank nonnegative constraints. Applying NMF into huge-size matrices, we specifically address stochastic multiplicative update (MU) rule, which is the most popular, but which has slow convergence property. This present paper introduces a gradient averaging technique of stochastic gradient on the stochastic MU rule, and proposes an accelerated stochastic multiplicative update rule: SAGMU. Extensive computational experiments using both synthetic and real-world datasets demonstrate the effectiveness of SAGMU.

Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference, EUSIPCO 2018
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages2593-2597
Number of pages5
ISBN (Electronic)9789082797015
DOIs
Publication statusPublished - 2018 Nov 29
Externally publishedYes
Event26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy
Duration: 2018 Sep 32018 Sep 7

Publication series

NameEuropean Signal Processing Conference
Volume2018-September
ISSN (Print)2219-5491

Conference

Conference26th European Signal Processing Conference, EUSIPCO 2018
CountryItaly
CityRome
Period18/9/318/9/7

Keywords

  • Gradient averaging
  • Multiplicative update
  • Nonnegative matrix factorization
  • Stochastic gradient

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Kasai, H. (2018). Accelerated stochastic multiplicative update with gradient averaging for nonnegative matrix factorizations. In 2018 26th European Signal Processing Conference, EUSIPCO 2018 (pp. 2593-2597). [8553610] (European Signal Processing Conference; Vol. 2018-September). European Signal Processing Conference, EUSIPCO. https://doi.org/10.23919/EUSIPCO.2018.8553610