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Code for our ICML '18 paper "Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization"

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Maximum Marginal Likelihood for Dictionary Learning in Gamma-Poisson models (MMLE-GaP)

Code related to the paper:

Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
Filstroff L., Lumbreras A., Févotte C.
International Conference on Machine Learning (2018)

R/Rcpp implementation of MMLE-GaP and related algorithms.

The main MCMC-EM algorithm is in R/MMLE_MCEM.R. From this file, calls are made to methods in other files that compute the Monte Carlo E-Step and the M-step. The basic MC E-Step is a Gibbs sampler, but we have also played with other alternatives. There are R and Rcpp implementations (src/ folder) that are much faster.

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Code for our ICML '18 paper "Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization"

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