Non-negative matrix factorization and its application in blind sparse source separation with less sensors than sources

Yuanqing Li, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Purpose - Proposes a non-negative matrix factorization method. Design/methodology approach - Presents an algorithm for finding a suboptimal basis matrix. This is controlled by data cluster centers which can guarantee that the coefficient is very sparse. This leads to the proposition of an application of non-matrix factorization for blind sparse source separation with less sensors than sources. Findings - Two simulation examples reveal the validity and performance of the algorithm in this paper. Originality/value - Using the approach in this paper, the sparse sources can be recovered even if the sources are overlapped to some degree.

Original languageEnglish
Pages (from-to)695-706
Number of pages12
JournalCOMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering
Volume24
Issue number2
DOIs
Publication statusPublished - 2005
Externally publishedYes

Keywords

  • Matrix algebra
  • Programming and algorithm theory

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