Factorization of Sparse Counts Matrices Through Poisson
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, <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.1811.01908>