Smoothing Technique and Fast Alternating Direction Method for Robust PCA

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.

关键词： Robust principal component analysis Smoothing technique Convex optimization Alternating direction method Moving object detection

作者: YANG Min

作者单位: College of Automation,Nanjing University of Posts and Telecommunications,Nanjing 210003

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 4782-4785

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）