Efficient Bayesian Model Selection in PARAFAC via Stochastic Thermodynamic Integration

Abstract : Parallel factor analysis (PARAFAC) is one of the most popular tensor factorization models. Even though it has proven successful in diverse application fields, the performance of PARAFAC usually hinges up on the rank of the factorization, which is typically specified manually by the practitioner. In this study, we develop a novel parallel and distributed Bayesian model selection technique for rank estimation in large-scale PARAFAC models. The proposed approach integrates ideas from the emerging field of stochastic gradient Markov Chain Monte Carlo, statistical physics, and distributed stochastic optimization. As opposed to the existing methods, which are based on some heuristics, our method has a clear mathematical interpretation, and has significantly lower computational requirements, thanks to data subsampling and parallelization. We provide formal theoretical analysis on the bias induced by the proposed approach. Our experiments on synthetic and large-scale real datasets show that our method is able to find the optimal model order while being significantly faster than the state-of-the-art.
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Article dans une revue
IEEE Signal Processing Letters, 2018
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Contributeur : Thanh Huy Nguyen <>
Soumis le : jeudi 26 avril 2018 - 12:13:16
Dernière modification le : jeudi 17 mai 2018 - 01:15:37
Document(s) archivé(s) le : jeudi 20 septembre 2018 - 05:22:48

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  • HAL Id : hal-01779074, version 1

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Thanh Huy Nguyen, Umut Simsekli, Gael Richard, Ali Cemgil. Efficient Bayesian Model Selection in PARAFAC via Stochastic Thermodynamic Integration. IEEE Signal Processing Letters, 2018. 〈hal-01779074〉

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