Creates a low-rank factorization of a sparse counts matrix by maximizing Poisson likelihood with l1/l2 regularization with all non-negative latent factors (e.g. for recommender systems or topic modeling) (Cortes, (2018) <arXiv:1811.01908>). Similar to hierarchical Poisson factorization, but follows an optimization-based approach with regularization instead of a hierarchical structure, and is fit through either proximal gradient or conjugate gradient instead of variational inference.
| Version: | 0.2.0 |
| Imports: | Matrix, methods |
| Enhances: | SparseM |
| Published: | 2020-05-26 |
| Author: | David Cortes |
| Maintainer: | David Cortes <david.cortes.rivera at gmail.com> |
| BugReports: | https://github.com/david-cortes/poismf/issues |
| License: | BSD_2_clause + file LICENSE |
| URL: | https://github.com/david-cortes/poismf |
| NeedsCompilation: | yes |
| CRAN checks: | poismf results |
| Reference manual: | poismf.pdf |
| Package source: | poismf_0.2.0.tar.gz |
| Windows binaries: | r-devel: poismf_0.2.0.zip, r-release: poismf_0.2.0.zip, r-oldrel: poismf_0.2.0.zip |
| macOS binaries: | r-release: poismf_0.2.0.tgz, r-oldrel: poismf_0.2.0.tgz |
| Old sources: | poismf archive |
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