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Institut de Recherche en Informatique de Toulouse |  |
Conference papers
Dictionary Learning with Statistical Sparsity in the Presence of Noise
Abstract : We consider a new stochastic formulation of sparse representations that is based on the family of symmetric α-stable (SαS) 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 SαS law from a set of its noisy samples. We assess the robustness of our algorithm with numerical examples.
Cited literature [19 references]
https://hal.archives-ouvertes.fr/hal-02966135
Contributor : Emmanuel Soubies <>
Submitted on : Tuesday, October 13, 2020 - 6:51:22 PM
Last modification on : Wednesday, October 21, 2020 - 3:38:17 AM
Long-term archiving on: : Thursday, January 14, 2021 - 7:50:16 PM
Contributor : Emmanuel Soubies <>
Submitted on : Tuesday, October 13, 2020 - 6:51:22 PM
Last modification on : Wednesday, October 21, 2020 - 3:38:17 AM
Long-term archiving on: : Thursday, January 14, 2021 - 7:50:16 PM
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Aziznejad_Eusipco_2020.pdf
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