Discrete Uncertainty Principles and Sparse Signal Processing

被引：7

作者：

Bandeira, Afonso S. ^{[1
]}

Lewis, Megan E. ^{[2
]}

Mixon, Dustin G. ^{[3
]}

机构：

[1] NYU, Courant Inst Math Sci, Dept Math, 251 Mercer St, New York, NY USA

[2] US Air Force, Operat Test & Evaluat Ctr, Detachment 5, Edwards AFB, CA USA

[3] US Air Force, Inst Technol, Dept Math & Stat, Wright Patterson AFB, OH 45433 USA

来源：

JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS | 2018年 / 24卷 / 04期

关键词：

Uncertainty principle; Sparsity; Compressed sensing; RESTRICTED ISOMETRY PROPERTY; RECOVERY; FOURIER; SHARP; DICTIONARIES;

D O I：

10.1007/s00041-017-9550-x

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

We develop new discrete uncertainty principles in terms of numerical sparsity, which is a continuous proxy for the 0-norm. Unlike traditional sparsity, the continuity of numerical sparsity naturally accommodates functions which are nearly sparse. After studying these principles and the functions that achieve exact or near equality in them, we identify certain consequences in a number of sparse signal processing applications.

引用

页码：935 / 956

页数：22

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