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, ). 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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("poismf")

0.1.1 by David Cortes, 24 days ago


Browse source code at https://github.com/cran/poismf


Authors: David Cortes


Documentation:   PDF Manual  


BSD_2_clause + file LICENSE license


Imports Rcpp, Matrix, SparseM, methods, nonneg.cg

Linking to Rcpp


See at CRAN