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.


Reference manual

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0.1.3 by David Cortes, 6 months 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