Nonnegative matrix factorization: A comprehensive review

Yu Xiong Wang, Yu Jin Zhang

Research output: Contribution to journalReview articlepeer-review

Abstract

Nonnegative Matrix Factorization (NMF), a relatively novel paradigm for dimensionality reduction, has been in the ascendant since its inception. It incorporates the nonnegativity constraint and thus obtains the parts-based representation as well as enhancing the interpretability of the issue correspondingly. This survey paper mainly focuses on the theoretical research into NMF over the last 5 years, where the principles, basic models, properties, and algorithms of NMF along with its various modifications, extensions, and generalizations are summarized systematically. The existing NMF algorithms are divided into four categories: Basic NMF (BNMF), Constrained NMF (CNMF), Structured NMF (SNMF), and Generalized NMF (GNMF), upon which the design principles, characteristics, problems, relationships, and evolution of these algorithms are presented and analyzed comprehensively. Some related work not on NMF that NMF should learn from or has connections with is involved too. Moreover, some open issues remained to be solved are discussed. Several relevant application areas of NMF are also briefly described. This survey aims to construct an integrated, state-of-the-art framework for NMF concept, from which the follow-up research may benefit.

Original languageEnglish (US)
Article number6165290
Pages (from-to)1336-1353
Number of pages18
JournalIEEE Transactions on Knowledge and Data Engineering
Volume25
Issue number6
DOIs
StatePublished - Jun 2013
Externally publishedYes

Keywords

  • Data mining
  • Dimensionality reduction
  • Multivariate data analysis
  • Nonnegative matrix factorization (NMF)

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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