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Analysis of Orthogonal Matching Pursuit Using the Restricted Isometry Property
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.
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Cited by
Chaos and compressive sensing based novel image encryption scheme
Khan, J. S., & Kayhan, S. K. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2020.102711 · Cited by 65
Sparsity-Aware Learning and Compressed Sensing: An Overview
Theodoridis, S., Kopsinis, Y., & Slavakis, K. (2014) · Academic Press Library in Signal Processing · doi:10.1016/b978-0-12-396502-8.00023-1 · Cited by 14
Metadata
- Authors
- Davenport, M. A., & Wakin, M. B.
- Venue
- IEEE Transactions on Information Theory
- Published
- 2010-09
- Volume
- 56
- Issue
- 9
- Pages
- 4395-4401
- ISSN
- 0018-9448 (ISSN-L)
- 1557-9654
- OpenAlex
- W2134474909
- Cited by
- 519
- References
- 0
- FWCI
- 37.27
- Type
- journal article
- Language
- en
- Open access
- closed
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