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Robust tensor factorization with unknown noise
Chen XA(陈希爱); Han Z(韩志); Wang, Yao; Zhao, Qian; Meng, Deyu; Tang YD(唐延东)
Department机器人学研究室
Conference Name2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
Conference DateJune 26 - July 1, 2016
Conference PlaceLas Vegas, NV, United states
Author of Source2016-January
Source PublicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Publication PlaceWashington, DC
2016
Pages5213-5221
Indexed ByEI ; CPCI(ISTP)
EI Accession number20163702804679
WOS IDWOS:000400012305031
Contribution Rank1
ISSN1063-6919
ISBN978-1-4673-8851-1
AbstractBecause of the limitations of matrix factorization, such as losing spatial structure information, the concept of tensor factorization has been applied for the recovery of a low dimensional subspace from high dimensional visual data. Generally, the recovery is achieved by minimizing the loss function between the observed data and the factorization representation. Under different assumptions of the noise distribution, the loss functions are in various forms, like L1and L2norms. However, real data are often corrupted by noise with an unknown distribution. Then any specific form of loss function for one specific kind of noise often fails to tackle such real data with unknown noise. In this paper, we propose a tensor factorization algorithm to model the noise as a Mixture of Gaussians (MoG). As MoG has the ability of universally approximating any hybrids of continuous distributions, our algorithm can effectively recover the low dimensional subspace from various forms of noisy observations. The parameters of MoG are estimated under the EM framework and through a new developed algorithm of weighted low-rank tensor factorization (WLRTF). The effectiveness of our algorithm are substantiated by extensive experiments on both of synthetic data and real image data.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19197
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
Chen XA,Han Z,Wang, Yao,et al. Robust tensor factorization with unknown noise[C]//2016-January. Washington, DC:IEEE Computer Society,2016:5213-5221.
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