Generalized Orthogonal Matching Pursuit

By Jian Wang, Seokbeop Kwon, Byonghyo Shim. IEEE Transactions on Signal Processing, 2011

672 citations recorded by the source corpus.

As a greedy algorithm to recover sparse signals from compressed measurements, orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. In this paper, we introduce an extension of the OMP for pursuing efficiency in reconstructing sparse signals. Our approach, henceforth referred to as generalized OMP (gOMP), is literally a generalization of the OMP in the sense that multiple <i>N</i> indices are identified per iteration. Owing to the selection of multiple “correct” indices, the gOMP algorithm is finished with much smaller number of iterations when compared to the OMP. We show that the gOMP can perfectly reconstruct any <i>K</i>-sparse signals (<i>K</i> >; 1), provided that the sensing matrix satisfies the RIP with δ<sub>NK</sub> <; [(√<i>N</i>)/(√<i>K</i>+3√<i>N</i>)]. We also demonstrate by empirical simulations that the gOMP has excellent recovery performance comparable to <i>l</i><sub>1</sub>-minimization technique with fast processing speed and competitive computational complexity.

Subfields: Biomedical Engineering, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8521d4c6178d1c0238f6d431fed0fbff0c74eaa1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

  1. Bhaskar D. Rao
  2. Byonghyo Shim
  3. Jun Won Choi
  4. Yacong Ding
  5. Dong In Kim

In Computational Mechanics

  1. Byonghyo Shim
  2. Dong In Kim
  3. Jun Won Choi
  4. Yacong Ding
  5. Bhaskar D. Rao
  6. Zhilin Zhang
  7. David Paul Wipf
  8. Yonina C. Eldar

In Signal Processing

  1. Byonghyo Shim
  2. Dong In Kim
  3. Jun Won Choi
  4. Yacong Ding
  5. Bhaskar D. Rao
  6. Zhilin Zhang
  7. Yonina C. Eldar
  8. David Paul Wipf

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