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Tensor RPCA by Bayesian CP Factorization with Complex Noise
Luo Q(罗琼); Han Z(韩志); Chen XA(陈希爱); Wang Y(王尧); Meng DY(孟德宇); Liang D(梁栋); Tang YD(唐延东)
作者部门机器人学研究室
会议名称2017 IEEE International Conference on Computer Vision (ICCV)
会议日期October 22-29, 2017
会议地点Venice, Italy
会议录名称2017 IEEE International Conference on Computer Vision (ICCV)
出版者IEEE
出版地New York
2017
页码5029-5038
收录类别EI ; CPCI(ISTP)
EI收录号20180704803739
WOS记录号WOS:000425498405012
产权排序1
ISSN号2380-7504
ISBN号978-1-5386-1032-9
摘要The RPCA model has achieved good performances in various applications. However, two defects limit its effectiveness. Firstly, it is designed for dealing with data in matrix form, which fails to exploit the structure information of higher order tensor data in some pratical situations. Secondly, it adopts L1-norm to tackle noise part which makes it only valid for sparse noise. In this paper, we propose a tensor RPCA model based on CP decomposition and model data noise by Mixture of Gaussians (MoG). The use of tensor structure to raw data allows us to make full use of the inherent structure priors, and MoG is a general approximator to any blends of consecutive distributions, which makes our approach capable of regaining the low dimensional linear subspace from a wide range of noises or their mixture. The model is solved by a new proposed algorithm inferred under a variational Bayesian framework. The superiority of our approach over the existing state-of-the-art approaches is demonstrated by extensive experiments on both of synthetic and real data.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/21357
专题机器人学研究室
通讯作者Han Z(韩志)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China
2.University of Chinese Academy of Sciences, China
3.Xi'An Jiaotong University, China
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Luo Q,Han Z,Chen XA,et al. Tensor RPCA by Bayesian CP Factorization with Complex Noise[C]. New York:IEEE,2017:5029-5038.
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