poismf: Factorization of Sparse Counts Matrices Through Poisson Likelihood

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 gradient-based methods instead of variational inference.

Version: 0.3.1-3
Imports: Matrix (≥ 1.3), methods
Published: 2021-09-26
Author: David Cortes [aut, cre, cph], Jean-Sebastien Roy [cph] (Copyright holder of included tnc library), Stephen Nash [cph] (Copyright holder of included tnc library)
Maintainer: David Cortes <david.cortes.rivera at gmail.com>
BugReports: https://github.com/david-cortes/poismf/issues
License: BSD_2_clause + file LICENSE
Copyright: see file COPYRIGHTS
URL: https://github.com/david-cortes/poismf
NeedsCompilation: yes
CRAN checks: poismf results


Reference manual: poismf.pdf


Package source: poismf_0.3.1-3.tar.gz
Windows binaries: r-devel: poismf_0.3.1-3.zip, r-devel-UCRT: poismf_0.3.1-3.zip, r-release: poismf_0.3.1-3.zip, r-oldrel: poismf_0.3.1-3.zip
macOS binaries: r-release (arm64): poismf_0.3.1-3.tgz, r-release (x86_64): poismf_0.3.1-3.tgz, r-oldrel: poismf_0.3.1-3.tgz
Old sources: poismf archive


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