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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.
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Contributor : Emmanuel Soubies Connect in order to contact the contributor
Submitted on : Tuesday, October 13, 2020 - 6:51:22 PM
Last modification on : Wednesday, June 9, 2021 - 10:00:28 AM
Long-term archiving on: : Thursday, January 14, 2021 - 7:50:16 PM


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  • HAL Id : hal-02966135, version 1


Shayan Aziznejad, Emmanuel Soubies, Michael Unser. Dictionary Learning with Statistical Sparsity in the Presence of Noise. 28th European Signal Processing Conference - EUSIPCO 2020, Jan 2021, Amsterdam, Netherlands. pp.2026-2029. ⟨hal-02966135⟩



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