Formulating Beurling LASSO for Source Separation via Proximal Gradient Iteration
Signal Processing
2022-02-17 v1 Information Retrieval
Functional Analysis
Optimization and Control
Abstract
Beurling LASSO generalizes the LASSO problem to finite Radon measures regularized via their total variation. Despite its theoretical appeal, this space is hard to parametrize, which poses an algorithmic challenge. We propose a formulation of continuous convolutional source separation with Beurling LASSO that avoids the explicit computation of the measures and instead employs the duality transform of the proximal mapping.
Cite
@article{arxiv.2202.08082,
title = {Formulating Beurling LASSO for Source Separation via Proximal Gradient Iteration},
author = {Sören Schulze and Emily J. King},
journal= {arXiv preprint arXiv:2202.08082},
year = {2022}
}