Iterative Reconstrained Low-Rank Representation via Weighted Nonconvex Regularizer

Benefiting from the joint consideration of geometric structures and low-rank constraint, graph low-rank representation (GLRR) method has led to the state-of-the-art results in many applications. However, it faces the limitations that the structure of errors should be known a prior, the isolated cons...

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Vydané v:	IEEE access Ročník 6; s. 51693 - 51707
Hlavní autori:	Zheng, Jianwei, Lu, Cheng, Yu, Hongchuan, Wang, Wanliang, Chen, Shengyong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	accelerated proximal gradient Adaptation models Algorithms Approximation algorithms Clustering Cost function Graphical representations Iterative methods Laplace equations Low-rank representation (LRR) Noise generation Noise measurement power method Rational functions Regularization Robustness singular value thresholding Subtraction weighted nonconvex constraint
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
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