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 ...
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。
(CODE) Low-Rank Matrix Recovery and Completion via Convex Optimization 这个是来自http://blog.sina.com.cn/s/blog_631a4cc401012wah.html这个链接,我这里借用下,这个博客有个小小的问题,我更新域名后可以打开,这里记录一下,也分享一下。 如第一个zip文件的地址是http://perception.csl.uiuc.edu/matrix-ran...
Y. Cao and Y. Xie, "Low-rank matrix recovery in poisson noise," arXiv preprint arXiv:1407.0726, 2014.Y. Cao, Y. Xie, Low-rank matrix recovery in Poisson noise, in: 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2014, pp. 384- 388. http://dx.doi....
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],...
However, low rank matrix recovery by RPCA is born for the existence of large sparse noise, so its performance and applicability are limited in the presence of mixed noise. In this paper, a generalized robust principal component analysis with norm \\(l_{2,1}\\) model is proposed to solve...
Based on the success of low-rank matrix recovery which has been applied to statistical learning, computer vision and signal processing, this paper presents a novel low-rank matrix recovery algorithm with discriminant regularization. Standard low-rank matrix recovery algorithm decomposes the original ...
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)
This paper introduces a new HSI restoration method based on low-rank matrix recovery (LRMR), which can simultaneously remove the Gaussian noise, impulse noise, dead lines, and stripes. By lexicographically ordering a patch of the HSI into a 2-D matrix, the low-rank property of the ...
Low-Rank Matrix Recovery (LRMR) has recently been applied to saliency detection by decomposing image features into a low-rank component associated with background and a sparse component associated with visual salient regions. Despite its great potential, existing LRMR-based saliency detection methods ...