Effect of step-by-step estimation technique on uniqueness of solution in nonnegative matrix factorization minimizing quasi-L1 norm

Motoaki Mouri, Arao Funase, Andrzej Cichocki, Ichi Takumi, Hiroshi Yasukawa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Nonnegative matrix factorization (NMF) is a linear nonnegative approximate data representation technique. NMF is often used to solve blind signal separation (BSS) problem. We had used a basic NMF algorithm named ISRA and our original algorithm named QL1-NMF to analyze the environmental electromagnetic data in extremely low frequency (ELF) band. In previous research, we found that QL1-NMF works more robust than ISRA when our data includes many outliers. However, both algorithms have a problem that their solutions are not unique. In this paper, we try to estimate signals step-by-step. We research the effect which this technique has on the uniqueness of solutions.

Original languageEnglish
Title of host publicationICSP 2012 - 2012 11th International Conference on Signal Processing, Proceedings
Pages157-161
Number of pages5
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 11th International Conference on Signal Processing, ICSP 2012 - Beijing, China
Duration: 21 Oct 201225 Oct 2012

Publication series

NameInternational Conference on Signal Processing Proceedings, ICSP
Volume1

Conference

Conference2012 11th International Conference on Signal Processing, ICSP 2012
Country/TerritoryChina
CityBeijing
Period21/10/1225/10/12

Keywords

  • BSS
  • Initialization
  • NMF
  • Outlier
  • Uniqueness

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