Using Hankel Structured Low-Rank Approximation for Sparse Signal Recovery

Ivan Markovsky; Pier Luigi Dragotti

doi:10.1007/978-3-319-93764-9_44

ScienceGate Book Chapters

BOOK-CHAPTER

Using Hankel Structured Low-Rank Approximation for Sparse Signal Recovery

Ivan Markovsky Pier Luigi Dragotti

Year: 2018 Lecture notes in computer science Pages: 479-487 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-319-93764-9_44

Get Full-Text PDF Get Analytical Report

Keywords:

Low-rank approximation Hankel matrix Approximation algorithm Rank (graph theory) Regularization (linguistics) Computer science Matrix norm Algorithm System identification Applied mathematics Computational complexity theory Norm (philosophy) Mathematical optimization Mathematics Artificial intelligence Data modeling Combinatorics Mathematical analysis

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.12

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Statistical and numerical algorithms

Physical Sciences → Mathematics → Applied Mathematics

Sparse and Compressive Sensing Techniques

Physical Sciences → Engineering → Computational Mechanics

Image and Signal Denoising Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Using Hankel Structured Low-Rank Approximation for Sparse Signal Recovery

Metrics

Citation History

Topics

Related Documents

Positive Semidefinite Hankel Structured Low Rank Approximation using Vandermonde Decomposition

Optimal Hankel Structured Rank-1 Approximation

Random noise attenuation in seismic data using Hankel sparse low-rank approximation

A gradient system approach for Hankel structured low-rank approximation

Low-rank physical model recovery from low-rank signal approximation