Two efficient algorithms for approximately orthogonal nonnegative matrix factorization

Bo Li, Guoxu Zhou, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)

Abstract

Nonnegative matrix factorization (NMF) with orthogonality constraints is quite important due to its close relation with the K-means clustering. While existing algorithms for orthogonal NMF impose strict orthogonality constraints, in this letter we propose a penalty method with the aim of performing approximately orthogonal NMF, together with two efficient algorithms respectively based on the Hierarchical Alternating Least Squares (HALS) and the Accelerated Proximate Gradient (APG) approaches. Experimental evidence was provided to show their high efficiency and flexibility by using synthetic and real-world data.

Original languageEnglish
Article number6960861
Pages (from-to)843-846
Number of pages4
JournalIEEE Signal Processing Letters
Volume22
Issue number7
DOIs
Publication statusPublished - 1 Jul 2015
Externally publishedYes

Keywords

  • Accelerated proximal gradient
  • nonnegative matrix factorization

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