Aziznejad, ShayanSoubies, EmmanuelUnser, Michaël2021-05-192021-05-192021-05-192020-01-0110.23919/Eusipco47968.2020.9287767https://infoscience.epfl.ch/handle/20.500.14299/178105WOS:000632622300408We consider a new stochastic formulation of sparse representations that is based on the family of symmetric alpha-stable (S alpha S) distributions. Within this framework, we develop a novel dictionary-learning algorithm that involves a new estimation technique based on the empirical characteristic function. It finds the unknown parameters of an S alpha S law from a set of its noisy samples. We assess the robustness of our algorithm with numerical examples.dictionary learningsparse codingsparse representationstable distributionempirical characteristic functiontext::conference output::conference proceedings::conference paper