Title :
Analysis of Orthogonal Matching Pursuit Using the Restricted Isometry Property
Author :
Davenport, Mark A. ; Wakin, Michael B.
Author_Institution :
Dept. of Stat., Stanford Univ., Stanford, CA, USA
Abstract :
Orthogonal matching pursuit (OMP) is the canonical greedy algorithm for sparse approximation. In this paper we demonstrate that the restricted isometry property (RIP) can be used for a very straightforward analysis of OMP. Our main conclusion is that the RIP of order K+1 (with isometry constant δ <; [ 1/( 3√K)]) is sufficient for OMP to exactly recover any K-sparse signal. The analysis relies on simple and intuitive observations about OMP and matrices which satisfy the RIP. For restricted classes of K-sparse signals (those that are highly compressible), a relaxed bound on the isometry constant is also established. A deeper understanding of OMP may benefit the analysis of greedy algorithms in general. To demonstrate this, we also briefly revisit the analysis of the regularized OMP (ROMP) algorithm.
Keywords :
approximation theory; greedy algorithms; iterative methods; K-sparse signal; canonical greedy algorithm; isometry constant; orthogonal matching pursuit; regularized OMP algorithm; relaxed bound; restricted isometry property; sparse approximation; Algorithm design and analysis; Approximation algorithms; Approximation methods; Dictionaries; Greedy algorithms; Matching pursuit algorithms; Pursuit algorithms; Signal processing algorithms; Software algorithms; Software packages; Sparse matrices; Compressive sensing; greedy algorithms; orthogonal matching pursuit (OMP); redundant dictionaries; restricted isometry property (RIP); sparse approximation;
Journal_Title :
Information Theory, IEEE Transactions on
DOI :
10.1109/TIT.2010.2054653