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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)
作者部门机器人学研究室
会议名称7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
会议日期July 31 - August 4, 2017
会议地点Hawaii, USA
会议主办者IEEE Robotics and Automation Society
会议录名称2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
出版者IEEE
出版地New York
2017
页码37-41
收录类别EI ; CPCI(ISTP)
EI收录号20183905873607
WOS记录号WOS:000447628700007
产权排序1
ISBN号978-1-5386-0489-2
摘要

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.

语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/21351
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
2.University of Chinese Academy of Sciences
3.School of Mathematics and Statistics, Xi’an Jiaotong University
推荐引用方式
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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