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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Bibliographic Details

Published in:	IEEE Access
Main Authors:	Jianwei Zheng, Cheng Lu, Hongchuan Yu, Wanliang Wang, Shengyong Chen
Format:	Article
Language:	English
Published:	IEEE 2018-01-01
Subjects:	Low-rank representation (LRR) weighted nonconvex constraint accelerated proximal gradient singular value thresholding power method
Online Access:	https://ieeexplore.ieee.org/document/8466962/