Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications

Xiao Fu; Kejun Huang; Nikolaos Sidiropoulos; Wing Kin Ma

doi:10.1109/MSP.2018.2877582

Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications

Xiao Fu
, Kejun Huang
, Nikolaos Sidiropoulos
, Wing Kin Ma

Research output: Contribution to journal › Article › peer-review

255 Scopus citations

Abstract

Nonnegative matrix factorization (NMF) aims to factor a data matrix into low-rank latent factor matrices with nonnegativity constraints.

Original language	English (US)
Article number	8653529
Pages (from-to)	59-80
Number of pages	22
Journal	IEEE Signal Processing Magazine
Volume	36
Issue number	2
DOIs	https://doi.org/10.1109/MSP.2018.2877582
State	Published - Mar 2019
Externally published	Yes

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© 1991-2012 IEEE.

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10.1109/MSP.2018.2877582

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title = "Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications",

abstract = "Nonnegative matrix factorization (NMF) aims to factor a data matrix into low-rank latent factor matrices with nonnegativity constraints.",

author = "Xiao Fu and Kejun Huang and Nikolaos Sidiropoulos and Ma, \{Wing Kin\}",

note = "Publisher Copyright: {\textcopyright} 1991-2012 IEEE.",

year = "2019",

month = mar,

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Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications

Abstract

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