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Communication Dans Un Congrès Année : 2021

A Partially Collapsed Gibbs Sampler for Unsupervised Nonnegative Sparse Signal Restoration

Résumé

In this paper the problem of restoration of unsupervised nonnegative sparse signals is addressed in the Bayesian framework. We introduce a new probabilistic hierarchical prior, based on the Generalized Hyperbolic (GH) distribution, which explicitly accounts for sparsity. On the one hand, this new prior allows us to take into account the non-negativity. On the other hand, thanks to the decomposition of GH distributions as continuous Gaussian mean-variance mixture, a partially collapsed Gibbs sampler (PCGS) implementation is made possible, which is shown to be more efficient in terms of convergence time than the classical Gibbs sampler.
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Dates et versions

hal-03227140 , version 1 (17-05-2021)

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Citer

Mehdi Amrouche, Hervé Carfantan, Jérôme Idier. A Partially Collapsed Gibbs Sampler for Unsupervised Nonnegative Sparse Signal Restoration. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun 2021, Toronto, Canada. pp.5519-5523, ⟨10.1109/icassp39728.2021.9414293⟩. ⟨hal-03227140⟩
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