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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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| Publicado no: | Signal Processing |
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| Main Authors: | , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2018
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| Assuntos: | |
| Acesso em linha: | 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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