Dictionary Learning with Statistical Sparsity in the Presence of Noise
S. Aziznejad, E. Soubies, M. Unser
Proceedings of the Twenty-Eighth European Signal Processing Conference (EUSIPCO'20), Amsterdam, Kingdom of the Netherlands, Virtual, January 18-22, 2021, pp. 2026–2029.
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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AUTHOR="Aziznejad, S. and Soubies, E. and Unser, M.",
TITLE="Dictionary Learning with Statistical Sparsity in the Presence of
Noise",
BOOKTITLE="Proceedings of the Twenty-Eighth European Signal Processing
Conference ({EUSIPCO'20})",
YEAR="2021",
editor="",
volume="",
series="",
pages="2026--2029",
address="Amsterdam, Kingdom of the Netherlands",
month="January 18-22,",
organization="",
publisher="",
note="")
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