Low-Rank Matrix Recovery via Modified Schatten- Norm Minimization With Convergence Guarantees
In recent years, low-rank matrix recovery problems have attracted much attention in
computer vision and machine learning. The corresponding rank minimization problems are …
computer vision and machine learning. The corresponding rank minimization problems are …
Generalized nonconvex nonsmooth low-rank matrix recovery framework with feasible algorithm designs and convergence analysis
Decomposing data matrix into low-rank plus additive matrices is a commonly used strategy
in pattern recognition and machine learning. This article mainly studies the alternating …
in pattern recognition and machine learning. This article mainly studies the alternating …
[图书][B] Sparse optimization theory and methods
YB Zhao - 2018 - taylorfrancis.com
Seeking sparse solutions of underdetermined linear systems is required in many areas of
engineering and science such as signal and image processing. The efficient sparse …
engineering and science such as signal and image processing. The efficient sparse …
Global convergence guarantees of (A) GIST for a family of nonconvex sparse learning problems
In recent years, most of the studies have shown that the generalized iterated shrinkage
thresholdings (GISTs) have become the commonly used first-order optimization algorithms …
thresholdings (GISTs) have become the commonly used first-order optimization algorithms …
A new nonlocal low-rank regularization method with applications to magnetic resonance image denoising
Magnetic resonance (MR) images are frequently corrupted by Rician noise during image
acquisition and transmission. And it is very challenging to restore MR data because Rician …
acquisition and transmission. And it is very challenging to restore MR data because Rician …
Multiplicative noise removal: nonlocal low-rank model and its proximal alternating reweighted minimization algorithm
The goal of this paper is to develop a novel numerical method for efficient multiplicative
noise removal. The nonlocal self-similarity of natural images implies that the matrices formed …
noise removal. The nonlocal self-similarity of natural images implies that the matrices formed …
Image cartoon-texture decomposition by a generalized non-convex low-rank minimization method
HY Yan, Z Zheng - Journal of the Franklin Institute, 2024 - Elsevier
Image cartoon-texture decomposition is an important problem in image processing. In recent
years, by exploiting low-rank priors of images, low-rank minimization methods have been …
years, by exploiting low-rank priors of images, low-rank minimization methods have been …
Iteratively linearized reweighted alternating direction method of multipliers for a class of nonconvex problems
In this paper, we consider solving a class of nonconvex and nonsmooth problems frequently
appearing in signal processing and machine learning research. The traditional alternating …
appearing in signal processing and machine learning research. The traditional alternating …
Scalable proximal Jacobian iteration method with global convergence analysis for nonconvex unconstrained composite optimizations
The recent studies have found that the nonconvex relaxation functions usually perform better
than the convex counterparts in the l 0-norm and rank function minimization problems …
than the convex counterparts in the l 0-norm and rank function minimization problems …
Faster nonconvex low-rank matrix learning for image low-level and high-level vision: A unified framework
This study introduces a unified approach to tackle challenges in both low-level and high-
level vision tasks for image processing. The framework integrates faster nonconvex low-rank …
level vision tasks for image processing. The framework integrates faster nonconvex low-rank …