Convex Regularization and Representer Theorems
Optimization and Control
2018-12-12 v1 Information Theory
math.IT
Abstract
We establish a result which states that regularizing an inverse problem with the gauge of a convex set yields solutions which are linear combinations of a few extreme points or elements of the extreme rays of . These can be understood as the \textit{atoms} of the regularizer. We then explicit that general principle by using a few popular applications. In particular, we relate it to the common wisdom that total gradient variation minimization favors the reconstruction of piecewise constant images.
Cite
@article{arxiv.1812.04355,
title = {Convex Regularization and Representer Theorems},
author = {Claire Boyer and Antonin Chambolle and Yohann de Castro and Vincent Duval and Frédéric de Gournay and Pierre Weiss},
journal= {arXiv preprint arXiv:1812.04355},
year = {2018}
}
Comments
in Proceedings of iTWIST'18, Paper-ID: 30, Marseille, France, November, 21-23, 2018