# A Unified Framework for Constructing Nonconvex Regularizations

arXiv: https://arxiv.org/abs/2106.06123 · HTML: https://mlsanity.com/paper/2106.06123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [A Novel Regularization Based on the Error Function for Sparse Recovery](https://mlsanity.com/paper/2007.02784/) (2020)
- [Sparse recovery based on the generalized error function](https://mlsanity.com/paper/2105.13189/) (2021)

## Abstract (arXiv)

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.