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.
|Author||David Cortes [aut, cre, cph], Jean-Sebastien Roy [cph], Stephen Nash [cph]|
|Maintainer||David Cortes <firstname.lastname@example.org>|
|License||BSD_2_clause + file LICENSE|
|Package repository||View on CRAN|
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