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Sparse signal recovery using iterative proximal projection

Ghayem, F ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1109/TSP.2017.2778695
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2018
  4. Abstract:
  5. This paper is concerned with designing efficient algorithms for recovering sparse signals from noisy underdetermined measurements. More precisely, we consider minimization of a nonsmooth and nonconvex sparsity promoting function subject to an error constraint. To solve this problem, we use an alternating minimization penalty method, which ends up with an iterative proximal-projection approach. Furthermore, inspired by accelerated gradient schemes for solving convex problems, we equip the obtained algorithm with a so-called extrapolation step to boost its performance. Additionally, we prove its convergence to a critical point. Our extensive simulations on synthetic as well as real data verify that the proposed algorithm considerably outperforms some well-known and recently proposed algorithms. © 1991-2012 IEEE
  6. Keywords:
  7. Iterative sparsification-projection ; proximal splitting algorithms ; SL0 ; Sparse signal recovery ; Acceleration ; Compressed sensing ; Constrained optimization ; Cost functions ; Iterative methods ; Optimization ; Recovery ; Signal processing ; Convergence ; Iterative algorithm ; Signal processing algorithms ; Sparse signal recoveries ; Sparsification ; Splitting algorithms ; Signal reconstruction
  8. Source: IEEE Transactions on Signal Processing ; Volume 66, Issue 4 , 2018 , Pages 879-894 ; 1053587X (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/8123863