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, David, 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: 0.1.3
Imports: Rcpp (≥ 0.12.19), Matrix, SparseM, methods, nonneg.cg
LinkingTo: Rcpp
Published: 2019-09-05
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera at gmail.com>
License: BSD_2_clause + file LICENSE
NeedsCompilation: yes
CRAN checks: poismf results


Reference manual: poismf.pdf
Package source: poismf_0.1.3.tar.gz
Windows binaries: r-prerelease: poismf_0.1.3.zip, r-release: poismf_0.1.3.zip, r-oldrel: poismf_0.1.3.zip
macOS binaries: r-prerelease: poismf_0.1.3.tgz, r-release: poismf_0.1.3.tgz, r-oldrel: poismf_0.1.3.tgz
Old sources: poismf archive


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