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A Generalized Hard Thresholding Pursuit Algorithm

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Abstract

Compressed sensing ensures the accurate reconstruction of sparse signals from far fewer samples than required in the classical Shannon–Nyquist theorem. In this paper, a generalized hard thresholding pursuit (GHTP) algorithm is presented that can recover unknown vectors without the sparsity level information. We also analyze the convergence of the proposed algorithm. Numerical experiments are given for synthetic and real-world data to illustrate the validity and the good performance of the proposed algorithm.

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Acknowledgements

We are grateful to the authors of [15] for their code of the ASMP. The work was partially supported by Guangdong-National Ministry of Education IAR project (Grant 2012B091100331), NSFC-Guangdong union project (Grant U0835003), and the NSFC (Grants 61004054, 61104053 and 61103122).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, GuangZhou, 510641, China

    Haifeng Li, Yuli Fu, Qiheng Zhang & Rong Rong

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  1. Haifeng Li
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  2. Yuli Fu
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  4. Rong Rong
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Correspondence to Yuli Fu.

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Li, H., Fu, Y., Zhang, Q. et al. A Generalized Hard Thresholding Pursuit Algorithm. Circuits Syst Signal Process 33, 1313–1323 (2014). https://doi.org/10.1007/s00034-013-9694-0

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  • DOI: https://doi.org/10.1007/s00034-013-9694-0

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