Applications of sparse representation and compressive sensing

Richard G. Baraniuk, Emmanuel Candes, Michael Elad, Yi Ma

Research output: Contribution to journalArticle

101 Scopus citations

Abstract

Applications of sparse representation and compressive sensing are discussed. A sparse signal is a signal that can be represented as a linear combination of relatively few base elements in a basis or an over complete dictionary. The new theory of sparse representation and compressive sensing not only establishes a more rigorous mathematical framework for studying high-dimensional data, but also provides computationally feasible ways to uncover the structures of the data, giving rise to a large repertoire of efficient algorithms. The papers aim to provide good survey or review of past achievements in the field, or feature some new exciting developments by the authors, or discuss promising new directions and extensions. The new theory of sparse representation and compressive sensing not only establishes a more rigorous mathematical framework for studying high-dimensional data, but also provides computationally feasible ways to uncover the structures of the data, giving rise to a large repertoire of efficient algorithms.

Original languageEnglish (US)
Article number5466604
Pages (from-to)906-909
Number of pages4
JournalProceedings of the IEEE
Volume98
Issue number6
DOIs
StatePublished - Jun 2010

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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