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

IEEE Signal Processing Magazine (2019) 36(2) 59-80
DOI: 10.1109/MSP.2018.2877582
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Abstract

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

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Fu, X., Huang, K., Sidiropoulos, N. D., & Ma, W. K. (2019). Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications. IEEE Signal Processing Magazine, 36(2), 59–80. https://doi.org/10.1109/MSP.2018.2877582

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