A new greedy algorithm for sparse recovery

Compressed sensing (CS) has been one of the great successes of applied mathematics in the last decade. This paper proposes a new method, combining the advantage of the Compressive Sampling Matching Pursuit (CoSaMP) algorithm and the Quasi–Newton Iteration Projection (QNIP) algorithm, for the recover...

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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 275; S. 137 - 143
Hauptverfasser: Wang, Qian, Qu, Gangrong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 31.01.2018
Schlagworte:
Compressed sensing
Greedy algorithm
Restricted isometry property
Signal processing
Sparse signal
Sparse signal
Signal processing
Greedy algorithm
Restricted isometry property
Compressed sensing
ISSN:0925-2312, 1872-8286
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Zusammenfassung:Compressed sensing (CS) has been one of the great successes of applied mathematics in the last decade. This paper proposes a new method, combining the advantage of the Compressive Sampling Matching Pursuit (CoSaMP) algorithm and the Quasi–Newton Iteration Projection (QNIP) algorithm, for the recovery of sparse signal from underdetermined linear systems. To get the new algorithm, Quasi–Newton Projection Pursuit (QNPP), the least-squares technique in CoSaMP is used to accelerate convergence speed and QNIP is modified slightly. The convergence rate of QNPP is studied, under a certain condition on the restricted isometry constant of the measurement matrix, which is smaller than that of QNIP. The fast version of QNPP is also proposed which uses the Richardson’s iteration to reduce computation time. The numerical results show that the proposed algorithms have higher recovery rate and faster convergence speed than existing techniques.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2017.05.022