Boundedness of modified multiplicative updates for nonnegative matrix factorization

Jiro Katayama, Norikazu Takahashi, Jun'Ichi Takeuchi

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

3 Citations (Scopus)

Abstract

There have been proposed various types of multiplicative updates for nonnegative matrix factorization. However, these updates have a serious drawback in common: they are not defined for all pairs of nonnegative matrices. Furthermore, due to this drawback, their global convergence in the sense of Zangwill's theorem cannot be proved theoretically. In this paper, we consider slightly modified versions of various multiplicative update rules, that are defined for all pairs of matrices in the domain, and show that many of them have the boundedness property. This property is a necessary condition for update rules to be globally convergent in the sense of Zangwill's theorem.

Original languageEnglish
Title of host publication2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Pages252-255
Number of pages4
DOIs
Publication statusPublished - Dec 1 2013
Event2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 - Saint Martin, France
Duration: Dec 15 2013Dec 18 2013

Publication series

Name2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013

Other

Other2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
CountryFrance
CitySaint Martin
Period12/15/1312/18/13

ASJC Scopus subject areas

  • Computer Science Applications

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  • Cite this

    Katayama, J., Takahashi, N., & Takeuchi, JI. (2013). Boundedness of modified multiplicative updates for nonnegative matrix factorization. In 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 (pp. 252-255). [6714055] (2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013). https://doi.org/10.1109/CAMSAP.2013.6714055