A unified framework for sparse non-negative least squares using multiplicative updates and the non-negative matrix factorization problem

We study the sparse non-negative least squares (S-NNLS) problem. S-NNLS occurs naturally in a wide variety of applications where an unknown, non-negative quantity must be recovered from linear measurements. We present a unified framework for S-NNLS based on a rectified power exponential scale mixtur...

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Vydáno v:Signal Processing
Hlavní autoři: Fedorov, Igor, Nalci, Alican, Giri, Ritwik, Rao, Bhaskar D., Nguyen, Truong Q., Garudadri, Harinath
Médium: Artigo
Jazyk:Inglês
Vydáno: 2018
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On-line přístup:https://ncbi.nlm.nih.gov/pmc/articles/PMC6590072/
https://ncbi.nlm.nih.gov/pubmed/31235988
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.sigpro.2018.01.001
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