In this chapter, we discuss a problem of estimation of a large target matrix based 4 on a finite number of noisy measurements of linear functionals (often, random) 5 of this matrix. The underlying assumption is that the target matrix is of small 6 rank and the goal is to determine how ...
随后,在接下来的理论证明中,作者们证明了上述问题的答案是肯定的,在 matrix and bilinear sensing 与 matrix completion 任务中,with high probability,我们得到的解同时具备 nearly zero training loss, norm-minimal, flat minima 这三个特点。
Over the past decade, low-rank matrix recovery (LRMR) problem has attracted considerable interest of researchers in many fields, including computer vision [1], recommender systems [2], and machine learning [3], to name a few. Mathematically, this problem aims to recover an unknown low-rank ...
Low-rank matrix recovery problem is difficult due to its non-convex properties and it is usually solved using convex relaxation approaches. In this paper, we formulate the non-convex low-rank matrix recovery problem exactly using novel Ky Fan 2-k-norm-based models. A general difference of conv...
如第一个zip文件的地址是http://perception.csl.uiuc.edu/matrix-rank/Files/inexact_alm_rpca.zip,但这个地址是打不开的,将uiuc修改为Illinois,就可以下载了。 该博客所有信息都是正确的,应该是原先两个域名都可以使用,现在uiuc不能用了,修改为Illinois就可以。
低秩矩阵恢复算法综述 survey on algorithms of low-rank matrix recovery.pdf,第30卷第6期 计算机应用研究 V01.30No.6 2013年6月 Researchof Jun.2013 Application Computers 低秩矩阵恢复算法综述 史加荣,郑秀云,魏宗田,杨威 (西安建筑科技大学理学院,西安710055)
Over the past decade, the low rank matrix recovery (LRMR) problem, which aims to recover a low rank matrix from its linear observations, has attracted extensive research. It has been applied to various areas, such as recommendation systems, image processing, machine learning, etc [1], [2],...
Leung. Guarantees of Riemannian Optimization for Low Rank Matrix Recovery. SIAM J. Matrix Anal. Appl., 37(3):1198-1222, 2016.K. Wei, J.-F. Cai, T. F. Chan, and S. Leung, "Guarantees of riemannian optimization for low rank matrix recovery," SIAM J. Matrix Anal. Appl., vol. 37...
approximation conjugate gradient method; low-rank matrix recovery; backtracking linear search technique; global convergence; superlinear convergence MSC: 49M37; 65K05; 90C30; 90C56 1. Introduction Problem Description Motivation The low-rank matrix plays an important role in a broad range of ...
CVPR2012文章阅读(2)-A Unified Approach to Salient Object Detection via Low Rank Matrix Recovery 本文是CVPR的oral文章,是对低秩矩阵重构在显著性方面的应用。这篇文章的三个创新点:1,提出了一个新的图像表达方式。通过分割和特征转换学习,本文的模型基于底秩矩阵重建理论。这个模型提供了一个新的显著性提取...