Sparse Representation and Low-Rank Approximation for Sensor Signal Processing

被引：0

作者：

Zhu, Yanping ^{[1
]}

Jiang, Aimin ^{[2
]}

Liu, Xiaofeng ^{[2
]}

Kwan, Hon Keung ^{[3
]}

机构：

[1] Changzhou Univ, Sch Informat Sci & Engn, Changzhou, Peoples R China

[2] Hohai Univ, Coll Internet Things Engn, Changzhou, Peoples R China

[3] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON, Canada

来源：

2017 IEEE 30TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE) | 2017年

关键词：

RECOVERY;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Sparsity and low-rank structures are recently considered as an important property in various signal processing problems. They have been widely applied in image processing, communication, computer vision, pattern recognition, radar, etc. The main purpose of this paper is to provide a review on sparse representation and low-rank approximation, and their applications in sensor signal processing. Three specific scenarios of sensor signal processing are further discussed. Simulations and experiments are presented in each signal processing scenario to demonstrate the capability of sparse representation and low-rank approximation.

引用

页数：5

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