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conference paper
Dictionary Learning with Statistical Sparsity in the Presence of Noise
January 1, 2020
28Th European Signal Processing Conference (Eusipco 2020)
We 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.
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Name
aziznejad2101.pdf
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Publisher's version
Access type
openaccess
License Condition
CC BY-NC-ND
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385.87 KB
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Adobe PDF
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