A Unified Framework for Constructing Nonconvex Regularizations
Machine Learning
2022-02-16 v1 Information Theory
Machine Learning
math.IT
Abstract
Over the past decades, many individual nonconvex methods have been proposed to achieve better sparse recovery performance in various scenarios. However, how to construct a valid nonconvex regularization function remains open in practice. In this paper, we fill in this gap by presenting a unified framework for constructing the nonconvex regularization based on the probability density function. Meanwhile, a new nonconvex sparse recovery method constructed via the Weibull distribution is studied.
Keywords
Cite
@article{arxiv.2106.06123,
title = {A Unified Framework for Constructing Nonconvex Regularizations},
author = {Zhiyong Zhou},
journal= {arXiv preprint arXiv:2106.06123},
year = {2022}
}