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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)
Department机器人学研究室
Conference Name2017 IEEE International Conference on Computer Vision (ICCV)
Conference DateOctober 22-29, 2017
Conference PlaceVenice, Italy
Source Publication2017 IEEE International Conference on Computer Vision (ICCV)
PublisherIEEE
Publication PlaceNew York
2017
Pages5029-5038
Indexed ByEI ; CPCI(ISTP)
EI Accession number20180704803739
WOS IDWOS:000425498405012
Contribution Rank1
ISSN2380-7504
ISBN978-1-5386-1032-9
AbstractThe 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.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/21357
Collection机器人学研究室
Corresponding AuthorHan Z(韩志)
Affiliation1.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
Recommended Citation
GB/T 7714
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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