Robust Low-Rank Matrix Recovery Fusing Local-Smoothness
Author:
Affiliation:
1. School of Mathematics and Statistics, Southwest University, Chongqing, China
2. College of Mathematics & Information, China West Normal University, Nanchong, China
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
China Postdoctoral Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/97/9686799/09987678.pdf?arnumber=9987678
Reference40 articles.
1. Stable Image Reconstruction Using Total Variation Minimization
2. SpaRCS: Recovering low-rank and sparse matrices from compressive measurements;waters;Proc Neural Inf Process Syst,0
3. Adaptive Total-Variation Regularized Low-Rank Representation for Analyzing Single-Cell RNA-seq Data
4. Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements
5. Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
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