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A Generalized Model for Robust Tensor Factorization With Noise Modeling by Mixture of Gaussians
Chen XA(陈希爱); Han Z(韩志); Wang Y(王尧); Zhao Q(赵谦); Meng DY(孟德宇); Lin, Lin; Tang YD(唐延东)
Department机器人学研究室
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162-237X
2018
Volume29Issue:11Pages:5380-5393
Indexed BySCI ; EI
EI Accession number20181004879696
WOS IDWOS:000447832200017
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China ; Youth Innovation Promotion Association of the Chinese Academy of Sciences ; China Postdoctoral Science Foundation ; Key Research Program of Hunan Province, China
KeywordExpectation–maximization (Em) Algorithm Generalized Weighted Low-rank Tensor Factorization (Gwlrtf) Mixture Of Gaussians (Mog) Model Tensor Factorization
Abstract

The low-rank tensor factorization (LRTF) technique has received increasing attention in many computer vision applications. Compared with the traditional matrix factorization technique, it can better preserve the intrinsic structure information and thus has a better low-dimensional subspace recovery performance. Basically, the desired low-rank tensor is recovered by minimizing the least square loss between the input data and its factorized representation. Since the least square loss is most optimal when the noise follows a Gaussian distribution, L-norm-based methods are designed to deal with outliers. Unfortunately, they may lose their effectiveness when dealing with real data, which are often contaminated by complex noise. In this paper, we consider integrating the noise modeling technique into a generalized weighted LRTF (GWLRTF) procedure. This procedure treats the original issue as an LRTF problem and models the noise using a mixture of Gaussians (MoG), a procedure called MoG GWLRTF. To extend the applicability of the model, two typical tensor factorization operations, i.e., CANDECOMP/PARAFAC factorization and Tucker factorization, are incorporated into the LRTF procedure. Its parameters are updated under the expectation-maximization framework. Extensive experiments indicate the respective advantages of these two versions of MoG GWLRTF in various applications and also demonstrate their effectiveness compared with other competing methods.

Language英语
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS KeywordDIMENSIONALITY REDUCTION ; HYPERSPECTRAL IMAGES ; DECOMPOSITION ; RECOGNITION ; MOTION ; RANK
WOS Research AreaComputer Science ; Engineering
Funding ProjectNational Natural Science Foundation of China[61773367] ; National Natural Science Foundation of China[61303168] ; National Natural Science Foundation of China[11501440] ; National Natural Science Foundation of China[61333019] ; National Natural Science Foundation of China[61373114] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2016183] ; China Postdoctoral Science Foundation[2017M610628] ; Key Research Program of Hunan Province, China[2017GK2273]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21579
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, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China
Recommended Citation
GB/T 7714
Chen XA,Han Z,Wang Y,et al. A Generalized Model for Robust Tensor Factorization With Noise Modeling by Mixture of Gaussians[J]. IEEE Transactions on Neural Networks and Learning Systems,2018,29(11):5380-5393.
APA Chen XA.,Han Z.,Wang Y.,Zhao Q.,Meng DY.,...&Tang YD.(2018).A Generalized Model for Robust Tensor Factorization With Noise Modeling by Mixture of Gaussians.IEEE Transactions on Neural Networks and Learning Systems,29(11),5380-5393.
MLA Chen XA,et al."A Generalized Model for Robust Tensor Factorization With Noise Modeling by Mixture of Gaussians".IEEE Transactions on Neural Networks and Learning Systems 29.11(2018):5380-5393.
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