Robust Alternative Minimization for Matrix Completion

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Robust Alternative Minimization for Matrix Completion
	Lu, Xiaoqiang 1; Gong, Tieliang 2; Yan, Pingkun 1; Yuan, Yuan1 ; Li, Xuelong 1
刊名	ieee transactions on systems man and cybernetics part b-cybernetics
	2012-06-01
卷号	42 期号:3 页码:939-949
关键词	Computer vision convex optimization image processing low-rank matrices matrix completion nuclear norm minimization pattern recognition singular value decomposition (SVD)
ISSN号	1083-4419
产权排序	1
合作状况	国内
中文摘要	recently, much attention has been drawn to the problem of matrix completion, which arises in a number of fields, including computer vision, pattern recognition, sensor network, and recommendation systems. this paper proposes a novel algorithm, named robust alternative minimization (ram), which is based on the constraint of low rank to complete an unknown matrix. the proposed ram algorithm can effectively reduce the relative reconstruction error of the recovered matrix. it is numerically easier to minimize the objective function and more stable for large-scale matrix completion compared with other existing methods. it is robust and efficient for low-rank matrix completion, and the convergence of the ram algorithm is also established. numerical results showed that both the recovery accuracy and running time of the ram algorithm are competitive with other reported methods. moreover, the applications of the ram algorithm to low-rank image recovery demonstrated that it achieves satisfactory performance.
英文摘要	recently, much attention has been drawn to the problem of matrix completion, which arises in a number of fields, including computer vision, pattern recognition, sensor network, and recommendation systems. this paper proposes a novel algorithm, named robust alternative minimization (ram), which is based on the constraint of low rank to complete an unknown matrix. the proposed ram algorithm can effectively reduce the relative reconstruction error of the recovered matrix. it is numerically easier to minimize the objective function and more stable for large-scale matrix completion compared with other existing methods. it is robust and efficient for low-rank matrix completion, and the convergence of the ram algorithm is also established. numerical results showed that both the recovery accuracy and running time of the ram algorithm are competitive with other reported methods. moreover, the applications of the ram algorithm to low-rank image recovery demonstrated that it achieves satisfactory performance.
学科主题	automation & control systems ; computer science ; artificial intelligence ; computer science ; cybernetics
WOS标题词	science & technology ; technology
类目[WOS]	automation & control systems ; computer science, artificial intelligence ; computer science, cybernetics
研究领域[WOS]	automation & control systems ; computer science
关键词[WOS]	low-rank matrix ; norm minimization ; recovery ; factorization ; algorithms ; programs ; motion
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000304163200029
公开日期	2012-09-03
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/20257]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China 2.Hubei Univ, Fac Math & Comp Sci, Wuhan 430062, Peoples R China
推荐引用方式 GB/T 7714	Lu, Xiaoqiang,Gong, Tieliang,Yan, Pingkun,et al. Robust Alternative Minimization for Matrix Completion[J]. ieee transactions on systems man and cybernetics part b-cybernetics,2012,42(3):939-949.
APA	Lu, Xiaoqiang,Gong, Tieliang,Yan, Pingkun,Yuan, Yuan,&Li, Xuelong.(2012).Robust Alternative Minimization for Matrix Completion.ieee transactions on systems man and cybernetics part b-cybernetics,42(3),939-949.
MLA	Lu, Xiaoqiang,et al."Robust Alternative Minimization for Matrix Completion".ieee transactions on systems man and cybernetics part b-cybernetics 42.3(2012):939-949.