Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer

Autor:	Wang, Zhi-Yong, So, Hing Cheung, Zoubir, Abdelhak M.
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	This paper presents a novel loss function referred to as hybrid ordinary-Welsch (HOW) and a new sparsity-inducing regularizer associated with HOW. We theoretically show that the regularizer is quasiconvex and that the corresponding Moreau envelope is convex. Moreover, the closed-form solution to its Moreau envelope, namely, the proximity operator, is derived. Compared with nonconvex regularizers like the lp-norm with 0
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.04762 Zobrazit plný text záznamu View this record from Arxiv