Nearly Optimal Bounds for Orthogonal Least Squares

In this paper, we study the orthogonal least squares (OLS) algorithm for sparse recovery. On one hand, we show that if the sampling matrix A satisfies the restricted isometry property of order K + 1 with isometry constant δ κ+ 1

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Veröffentlicht in:	IEEE transactions on signal processing 2017-10, Vol.65 (20), p.5347-5356
Hauptverfasser:	Wen, Jinming, Wang, Jian, Zhang, Qinyu
Format:	Artikel
Sprache:	eng
Schlagworte:	Approximation algorithms Greedy algorithms Indexes Matching pursuit algorithms orthogonal least squares (OLS) orthogonal matching pursuit (OMP) restricted isometry property (RIP) Signal processing algorithms Sparse recovery Sparse representation
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Beschreibung
Zusammenfassung:	In this paper, we study the orthogonal least squares (OLS) algorithm for sparse recovery. On one hand, we show that if the sampling matrix A satisfies the restricted isometry property of order K + 1 with isometry constant δ κ+ 1
ISSN:	1053-587X 1941-0476
DOI:	10.1109/TSP.2017.2728502