Publication

Dictionary Learning with Statistical Sparsity in the Presence of Noise

Michaël Unser, Shayan Aziznejad, Emmanuel Emilien Louis Soubies
2020
Conference Papers

Abstract

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.

Official source

https://infoscience.epfl.ch/entities/publication/8c460843-2ae5-4862-b4cf-c5ce111daf73

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Dictionary Learning with Statistical Sparsity in the Presence of Noise

Chat with Graph Search

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Kernel Methods and Similarity Learning Applied in Computational Chemistry

Sparse image reconstruction on the sphere: implications of a new sampling theorem