Recovery of Sparse Signals Using Multiple Orthogonal Least Squares

@article{Wang2017RecoveryOS,
  title={Recovery of Sparse Signals Using Multiple Orthogonal Least Squares},
  author={Jian Wang and Ping Li},
  journal={IEEE Transactions on Signal Processing},
  year={2017},
  volume={65},
  pages={2049-2062}
}
Sparse recovery aims to reconstruct sparse signals from compressed linear measurements. In this paper, we propose a sparse recovery algorithm called multiple orthogonal least squares (MOLS), which extends the well-known orthogonal least squares (OLS) algorithm by allowing multiple <inline-formula><tex-math notation="LaTeX">$L$</tex-math> </inline-formula> indices to be selected per iteration. Owing to its ability to catch multiple support indices in each selection, MOLS often converges in fewer… CONTINUE READING
Related Discussions
This paper has been referenced on Twitter 2 times. VIEW TWEETS

Citations

Publications citing this paper.
Showing 1-9 of 9 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 56 references

An improved RIP-based performance guarantee for sparse signal reconstruction with noise via orthogonal matching pursuit

2014 International Symposium on Information Theory and its Applications • 2014
View 3 Excerpts

Multipath Matching Pursuit

IEEE Transactions on Information Theory • 2014

Similar Papers

Loading similar papers…