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Infinite Factorial Dynamical Model

Isabel Valera ; Fran Francisco ; Lennart Svensson (Institutionen för signaler och system) ; Fernando Perez-Cruz
29th Annual Conference on Neural Information Processing Systems, NIPS 2015, Montreal, Canada, 7-12 December (1049-5258). Vol. 2015-January (2015), p. 1666-1674.
[Konferensbidrag, refereegranskat]

We propose the infinite factorial dynamic model (iFDM), a general Bayesian nonparametric model for source separation. Our model builds on the Markov Indian buffet process to consider a potentially unbounded number of hidden Markov chains (sources) that evolve independently according to some dynamics, in which the state space can be either discrete or continuous. For posterior inference, we develop an algorithm based on particle Gibbs with ancestor sampling that can be efficiently applied to a wide range of source separation problems. We evaluate the performance of our iFDM on four well-known applications: multitarget tracking, cocktail party, power disaggregation, and multiuser detection. Our experimental results show that our approach for source separation does not only outperform previous approaches, but it can also handle problems that were computationally intractable for existing approaches.



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Denna post skapades 2015-12-14. Senast ändrad 2016-06-13.
CPL Pubid: 228180

 

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