Successful And Failing Matrices For l-1 recovery of sparse vectors

Asaad, A. (2012) Successful And Failing Matrices For l-1 recovery of sparse vectors. Masters thesis, University of Buckingham.

[img]
Preview
 Text
2012 Aras Asaad.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Download (1MB) | Preview

Abstract

In this thesis we give an overview of the notion of compressed sensing together with some special types of compressed sensing matrices. We then investigate the Restricted Isometry property and the Null Space property which are two of the most well-known properties of compressed sensing matrices needed for sparse signal recovery. We show that when the Restricted Isometry constant is ‘small enough’ then we can recover sparse vectors by

Item Type: Thesis (Masters)
Uncontrolled Keywords: Compressed Sensing; Ristricted Isometry Property; RIP; Null Space Property NSP; Compressive sensing matrices
Subjects: Q Science > QA Mathematics
Divisions: School of Computing
Depositing User: Rachel Pollard
Date Deposited: 16 Mar 2020 11:14
Last Modified: 16 Mar 2020 11:14
URI: http://bear.buckingham.ac.uk/id/eprint/469

Actions (login required)

View Item View Item
Buckingham E-Archive of Research is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.