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.

Getting started

Package details

AuthorDavid Cortes [aut, cre, cph], Jean-Sebastien Roy [cph], Stephen Nash [cph]
MaintainerDavid Cortes <david.cortes.rivera@gmail.com>
LicenseBSD_2_clause + file LICENSE
URL https://github.com/david-cortes/poismf
Package repositoryView on CRAN
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poismf documentation built on Jan. 13, 2021, 6:46 a.m.