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Sparse signal reconstruction via generalized two-stage thresholding

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References

  1. Donoho D L. Compressed sensing. IEEE Trans Inform Theor, 2006, 52: 1289–1306

    Article  MathSciNet  MATH  Google Scholar 

  2. Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM Rev, 2001, 43: 129–159

    Article  MathSciNet  MATH  Google Scholar 

  3. Wang Y, Meng D, Yuan M. Sparse recovery: from vectors to tensors. Natl Sci Rev, 2018, 5: 756–767

    Article  Google Scholar 

  4. Wang Y, Yin W. Sparse signal reconstruction via iterative support detection. SIAM J Imag Sci, 2010, 3: 462–491

    Article  MathSciNet  MATH  Google Scholar 

  5. Garg R, Khandekar R. Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property. In: Proceedings of the 26th Annual International Conference on Machine Learning, 2009. 337–344

  6. Foucart S. Hard thresholding pursuit: an algorithm for compressive sensing. SIAM J Numer Anal, 2011, 49: 2543–2563

    Article  MathSciNet  MATH  Google Scholar 

  7. Yuan X, Li P, Zhang T. Gradient hard thresholding pursuit. J Mach Learn Res, 2018, 18: 1–43

    MathSciNet  MATH  Google Scholar 

  8. Huang J, Jiao Y, Liu Y, et al. A constructive approach to L0 penalized regression. J Mach Learn Res, 2018, 19: 1–37

    MathSciNet  Google Scholar 

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Acknowledgements

This work was supported by Beijing Municipal Natural Science Foundation (Grant No. 4194076), National Natural Science Foundation of China (Grant Nos. U1836220, 61672267), Jiangsu Province Natural Science Foundation (Grant No. BK20170558), and China Scholarship Council (Grant No. 202008320094).

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Correspondence to Heping Song or Yuping Lai.

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Appendixes A–C. The supporting information is available online at http://info.scichina.com and http://link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Song, H., Ai, Z., Lai, Y. et al. Sparse signal reconstruction via generalized two-stage thresholding. Sci. China Inf. Sci. 65, 139303 (2022). https://doi.org/10.1007/s11432-020-3126-7

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  • DOI: https://doi.org/10.1007/s11432-020-3126-7

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