5 papers
Jensen-Bregman split Gibbs sampling
Nicolas Dobigeon, Mame Diarra Fall, Eric Barat
Split Gibbs sampling (SGS) is a versatile tool to sample from complex posterior distributions, such as those arising in imaging inverse problems. However, existing SGS instances ar…
Bridging data-driven priors via the score function for posterior sampling -- Comparative review and experimental study
Elhadji Cisse Faye, Mame Diarra Fall, Sylvain Delchini +1
This paper reviews how a diverse set of popular data-driven priors commonly used in Bayesian inverse problems can be unified through their respective score functions. By framing th…
Bregman geometry-aware split Gibbs sampling for Bayesian Poisson inverse problems
Elhadji Cisse Faye, Mame Diarra Fall, Nicolas Dobigeon +1
This paper proposes a novel Bayesian framework for solving Poisson inverse problems by devising a Monte Carlo sampling algorithm which accounts for the underlying non-Euclidean geo…
Online simplex-structured matrix factorization
Hugues Kouakou, José Henrique de Morais Goulart, Raffaele Vitale +4
Simplex-structured matrix factorization (SSMF) is a common task encountered in signal processing and machine learning. Minimum-volume constrained unmixing (MVCU) algorithms are amo…
On-the-fly spectral unmixing based on Kalman filtering
Hugues Kouakou, José Henrique de Morais Goulart, Raffaele Vitale +4
This work introduces an on-the-fly (i.e., online) linear unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After derivin…