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Robust tensor factorization with MRF under complex noise
Chen XA(陈希爱); Han Z(韩志); Shen GP(沈贵萍); Wang Y(王尧); Tang YD(唐延东)
Department机器人学研究室
Conference Name7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
Conference DateJuly 31 - August 4, 2017
Conference PlaceHawaii, USA
Author of SourceIEEE Robotics and Automation Society
Source Publication2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
PublisherIEEE
Publication PlaceNew York
2017
Pages37-41
Indexed ByEI ; CPCI(ISTP)
EI Accession number20183905873607
WOS IDWOS:000447628700007
Contribution Rank1
ISBN978-1-5386-0489-2
Abstract

Because of the limitations of matrix factorization, such as losing spatial structure information, the concept of low-rank tensor factorization (LRTF) has been applied for the recovery of a low dimensional subspace from high dimensional visual data. However, existing methods often fail to tackle the real data which are corrupted by the noise with unknown distribution. In this paper, we propose a novel noise model to the tensor case for the LRTF task to overcome the drawbacks of existing models. This procedure treats the target data as high-order tensor directly and models the noise by a Mixture of Gaussians and a Markov Random Field, which is called MoG WLRTF MRF. The parameters in the model are estimated under the variational EM framework. Extensive experiments demonstrate the effectiveness of our method compared with other competing methods.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/21351
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
2.University of Chinese Academy of Sciences
3.School of Mathematics and Statistics, Xi’an Jiaotong University
Recommended Citation
GB/T 7714
Chen XA,Han Z,Shen GP,et al. Robust tensor factorization with MRF under complex noise[C]//IEEE Robotics and Automation Society. New York:IEEE,2017:37-41.
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