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Parallelized Stochastic Gradient Markov Chain Monte Carlo algorithms for non-negative matrix factorization
2017
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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 nonnegative 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
doi:10.1109/icassp.2017.7952555
dblp:conf/icassp/SimsekliDBRMC17
fatcat:362nxrdr7fghfl572uuej4pkji