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K-step analysis of orthogonal greedy algorithms for non-negative sparse representations

Abstract : This paper proposes an exact recovery analysis of greedy algorithms for non-negative sparse representations. Orthogonal greedy algorithms such as Orthogonal Matching Pursuit (OMP) and Orthogonal Least Squares (OLS) consist of gradually increasing the solution support and updating the nonzero coefficients in the least squares sense. From a theoretical viewpoint, greedy algorithms have been extensively studied in terms of exact support recovery. In contrast, the exact recovery analysis of their non-negative extensions (NNOMP, NNOLS) remains an open problem. We show that when the mutual coherence mu is lower than 1/(2K-1), the iterates of NNOMP / NNOLS coincide with those of OMP / OLS, respectively, the latter being known to reach K-step exact recovery. Our analysis heavily relies on a sign preservation property satisfied by OMP and OLS. This property is of stand-alone interest and constitutes our second important contribution. Finally, we provide an extended discussion of the main challenges of deriving improved analyses for correlated dictionaries.
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https://hal.archives-ouvertes.fr/hal-01971697
Contributor : Charles Soussen Connect in order to contact the contributor
Submitted on : Tuesday, July 20, 2021 - 11:23:21 AM
Last modification on : Tuesday, September 21, 2021 - 4:06:15 PM

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Thi Thanh Nguyen, Charles Soussen, Jérôme Idier, El-Hadi Djermoune. K-step analysis of orthogonal greedy algorithms for non-negative sparse representations. Signal Processing, Elsevier, 2021, 188, pp.108185. ⟨10.1016/j.sigpro.2021.108185⟩. ⟨hal-01971697v5⟩

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