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Parallelized Stochastic Gradient Markov Chain Monte Carlo Algorithms for Non-Negative Matrix Factorization

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.
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https://hal.archives-ouvertes.fr/hal-01416357
Contributor : Roland Badeau <>
Submitted on : Tuesday, February 7, 2017 - 11:28:56 AM
Last modification on : Friday, July 31, 2020 - 10:44:11 AM
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  • HAL Id : hal-01416357, version 1

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Umut Simsekli, Alain Durmus, Roland Badeau, Gael Richard, Éric Moulines, et al.. Parallelized Stochastic Gradient Markov Chain Monte Carlo Algorithms for Non-Negative Matrix Factorization. 42nd International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Mar 2017, New Orleans, United States. ⟨hal-01416357⟩

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