Parallelized Stochastic Gradient Markov Chain Monte Carlo algorithms for non-negative matrix factorization

Umut Simsekli; Alain Durmus; Roland Badeau; Gaël Richard; Eric Moulines; A. Taylan Cemgil

ICASSP 2017accepted0 citations

Parallelized Stochastic Gradient Markov Chain Monte Carlo algorithms for non-negative matrix factorization

Umut Simsekli, Alain Durmus, Roland Badeau, Gaël Richard, Eric Moulines, A. Taylan Cemgil

Abstract

Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods have become popular in modern data analysis problems due to their computational efficiency. Even though they have proved useful for many statistical models, the application of SG-MCMC to non-negative matrix factorization (NMF) models has not yet been extensively explored. In this study, we develop two parallel SG-MCMC algorithms for a broad range of NMF models. We exploit the conditional independence structure of the NMF models and utilize a stratified sub-sampling approach for enabling parallelization. We illustrate the proposed algorithms on an image restoration task and report encouraging results.

BibTeX

@inproceedings{icassp2017_parallelizedstoc,
  title = {Parallelized Stochastic Gradient Markov Chain Monte Carlo algorithms for non-negative matrix factorization},
  author = {Umut Simsekli and Alain Durmus and Roland Badeau and Gaël Richard and Eric Moulines and A. Taylan Cemgil},
  booktitle = {ICASSP 2017},
  year = {2017}
}