nnmf: Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) is a technique to factorize a matrix with nonnegative values into the product of two matrices. Parallel computing is an option to enhance the speed and high-dimensional and large scale (and/or sparse) data are allowed. Relevant papers include: Wang Y. X. and Zhang Y. J. (2012). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6), 1336-1353 <doi:10.1109/TKDE.2012.51> and Kim H. and Park H. (2008). Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2), 713-730 <doi:10.1137/07069239X>.
| Version: |
1.0 |
| Depends: |
R (≥ 4.0) |
| Imports: |
ClusterR, Matrix, osqp, parallel, quadprog, Rfast, Rfast2, Rglpk, sparcl, stats |
| Published: |
2026-01-09 |
| DOI: |
10.32614/CRAN.package.nnmf (may not be active yet) |
| Author: |
Michail Tsagris [aut, cre] |
| Maintainer: |
Michail Tsagris <mtsagris at uoc.gr> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| CRAN checks: |
nnmf results |
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