A Generalized Model for Robust Tensor Factorization With Noise Modeling by Mixture of Gaussians

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...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 29; H. 11; S. 5380 - 5393
Hauptverfasser:	Chen, Xi'ai, Han, Zhi, Wang, Yao, Zhao, Qian, Meng, Deyu, Lin, Lin, Tang, Yandong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.11.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Computational modeling Computer vision Data processing Expectation–maximization (EM) algorithm Factorization Gaussian distribution generalized weighted low-rank tensor factorization (GWLRTF) Indexes Learning systems Least squares mixture of Gaussians (MoG) model Modelling Noise Noise pollution Normal distribution Oligodendrocyte-myelin glycoprotein Optimization Outliers (statistics) Robustness Tensile stress tensor factorization
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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