Smoothing Technique and Fast Alternating Direction Method for Robust PCA

被引：0
|
作者：
Yang Min [1 ]
机构：
[1] Nanjing Univ Posts & Telecommun, Coll Automat, Nanjing 210003, Jiangsu, Peoples R China
来源：
2014 33RD CHINESE CONTROL CONFERENCE (CCC) | 2014年
关键词：
Robust principal component analysis; Smoothing technique; Convex optimization; Alternating direction method; Moving object detection; THRESHOLDING ALGORITHM;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The problem of recovering a low-rank matrix from a set of observations corrupted with gross sparse error is known as the robust principal component analysis (RPCA) and has many applications in computer vision. In this paper, smoothing technique is used to smooth the non-smooth terms in the objective function, and we develop the fast alternating direction method for solving RPCA. Moving object detection experiments and numerical results on impulsive sparse matrix data show that our algorithms are competitive to current state-of-the-art solvers for RPCA in terms of speed.
引用
收藏
页码：4782 / 4785
页数：4
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