Compressive Sensing and Structured Random Matrices

  title={Compressive Sensing and Structured Random Matrices},
  author={Holger Rauhut Hausdorff},
  • Holger Rauhut Hausdorff
  • Published 2009
These notes give a mathematical introduction to compressive sensing focusing on recovery using `1-minimization and structured random matrices. An emphasis is put on techniques for proving probabilistic estimates for condition numbers of structured random matrices. Estimates of this type are key to providing conditions that ensure exact or approximate recovery of sparse vectors using `1-minimization. 
Highly Influential
This paper has highly influenced 39 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 434 citations. REVIEW CITATIONS
251 Citations
105 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 251 extracted citations

434 Citations

Citations per Year
Semantic Scholar estimates that this publication has 434 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 105 references

Über dyadische Brüche

  • A. Khintchine
  • Math. Z. 18
  • 1923
Highly Influential
20 Excerpts

Probability in Banach Spaces

  • M. Ledoux, M. Talagrand
  • 1991
Highly Influential
8 Excerpts

Probability inequalities for empirical processes and a law of the iterated logarithm

  • K. Alexander
  • Ann. Probab. 12
  • 1984
Highly Influential
9 Excerpts

An algorithm for the machine calculation of complex Fourier series

  • J. Cooley, J. Tukey
  • Math. Comp. 19
  • 1965
Highly Influential
6 Excerpts


  • R. Coifman, F. Geshwind, Y. Meyer
  • Appl. Comput. Harmon. Anal. 10
  • 2001
Highly Influential
3 Excerpts

Similar Papers

Loading similar papers…