On the Performance of Sparse Recovery via L_p-minimization (0<=p <=1)

@article{Wang2011OnTP,
  title={On the Performance of Sparse Recovery via L_p-minimization (0<=p <=1)},
  author={Meng Wang and Weiyu Xu and Ao Tang},
  journal={IEEE Trans. Information Theory},
  year={2011},
  volume={57},
  pages={7255-7278}
}
It is known that a high-dimensional sparse vector x* in R^n can be recovered from low-dimensional measurements y= A^{m*n} x* (m<n) . In this paper, we investigate the recovering ability of l_p-minimization (0<=p<=1) as p varies, where l_p-minimization returns a vector with the least l_p ``norm'' among all the vectors x satisfying Ax=y. Besides analyzing the performance of strong recovery where l_p-minimization needs to recover all the sparse vectors up to certain sparsity, we also for the first… CONTINUE READING

From This Paper

Topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 34 CITATIONS, ESTIMATED 35% COVERAGE

98 Citations

01020'12'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 98 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…