Bayesian Nonnegative Matrix Factorization
Bayesian Nonnegative Matrix Factorization is a probabilistic matrix decomposition method that incorporates Bayesian inference to estimate nonnegative latent factors and their uncertainties by placing prior distributions over the factor matrices, thereby enabling principled uncertainty quantification and automatic model selection within the framework of nonnegative matrix factorization.
22
Publications
685
Citations
99
Authors
56
Institutions
Publications per year
2017–2026
8
Variants
Bayesian Nmf
22
Source localization for sparse array using nonnegative sparse Bayesian learning
Nan Hu, Bing Sun, Jiajun Wang et al. · Signal Processing · 2016 · 39 citations
1–5 of 22
99
2
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2
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2
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2
h-index
1 publications · 12 citations
1
h-index
1–5 of 99
56

Broad Institute
Cambridge, United States
2 h-index
14 publications
379 citations
Harvard University
Cambridge, United States
2 h-index
7 publications
162 citations
Massachusetts General Hospital
Boston, United States
2 h-index
11 publications
275 citations
Laboratoire Traitement et Communication de l’Information
Palaiseau, France
2 h-index
4 publications
102 citations
Université Paris-Saclay
Gif-sur-Yvette, France
2 h-index
4 publications
49 citations
Jiangsu University
Zhenjiang, China
1 h-index
1 publications
39 citations

Soochow University
Suzhou, China
1 h-index
3 publications
117 citations
KU Leuven
Leuven, Belgium
1 h-index
1 publications
16 citations
1–8 of 56
TASLP · 3 publications · 65 citations
3
h-index
Repository · 2 publications · 23 citations
2
h-index
1 publications · 31 citations
1
h-index
1 publications · 36 citations
1
h-index
1 publications · 20 citations
1
h-index
1–5