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	Matrix completion by Truncated Nuclear Norm Regularization
	Zhang, Debing ; Hu, Yao ; Ye, Jieping ; Li, Xuelong ; He, Xiaofei
	2012
会议名称	2012 ieee conference on computer vision and pattern recognition, cvpr 2012
会议日期	june 16, 2012 - june 21, 2012
会议地点	providence, ri, united states
页码	2192-2199
英文摘要	estimating missing values in visual data is a challenging problem in computer vision, which can be considered as a low rank matrix approximation problem. most of the recent studies use the nuclear norm as a convex relaxation of the rank operator. however, by minimizing the nuclear norm, all the singular values are simultaneously minimized, and thus the rank can not be well approximated in practice. in this paper, we propose a novel matrix completion algorithm based on the truncated nuclear norm regularization (tnnr)by only minimizing the smallest n-r singular values, where n is the number of singular values and r is the rank of the matrix. in this way, the rank of the matrix can be better approximated than the nuclear norm. we further develop an efficient iterative procedure to solve the optimization problem by using the alternating direction method of multipliers and the accelerated proximal gradient line search method. experimental results in a wide range of applications demonstrate the effectiveness of our proposed approach.
收录类别	CPCI（ISTP） ; EI
产权排序	3
会议录	2012 ieee conference on computer vision and pattern recognition, cvpr 2012
会议录出版者	ieee computer society, 2001 l street n.w., suite 700, washington, dc 20036-4928, united states
会议录出版地	united states
语种	英语
ISSN号	10636919
ISBN号	9781467312264
内容类型	会议论文
源URL	[http://ir.opt.ac.cn/handle/181661/20536]
专题	西安光学精密机械研究所_光学影像学习与分析中心
推荐引用方式 GB/T 7714	Zhang, Debing,Hu, Yao,Ye, Jieping,et al. Matrix completion by Truncated Nuclear Norm Regularization[C]. 见:2012 ieee conference on computer vision and pattern recognition, cvpr 2012. providence, ri, united states. june 16, 2012 - june 21, 2012.

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