Iteratively Reweighted Least Squares Minimization for Sparse Recovery

@inproceedings{Daubechies2010IterativelyRL,
  title={Iteratively Reweighted Least Squares Minimization for Sparse Recovery},
  author={Ingrid Daubechies and Ronald M. Devore and Massimo Fornasier and C. Sinan Gunturk},
  year={2010}
}
Under certain conditions (known as the Restricted Isometry Property or RIP) on the m×N matrix Φ (where m < N), vectors x ∈ R that are sparse (i.e. have most of their entries equal to zero) can be recovered exactly from y := Φx even though Φ(y) is typically an (N − m)dimensional hyperplane; in addition x is then equal to the element in Φ(y) of minimal l1-norm. This minimal element can be identified via linear programming algorithms. We study an alternative method of determining x, as the limit… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 527 CITATIONS, ESTIMATED 25% COVERAGE

Compassionately Conservative Normalized Cuts for Image Segmentation

VIEW 9 EXCERPTS
CITES METHODS, BACKGROUND & RESULTS
HIGHLY INFLUENCED

Harmonic Mean Iteratively Reweighted Least Squares for low-rank matrix recovery

  • 2017 International Conference on Sampling Theory and Applications (SampTA)
  • 2017
VIEW 10 EXCERPTS
CITES METHODS, BACKGROUND & RESULTS
HIGHLY INFLUENCED

Convergence of ℓ2/3 Regularization for Sparse Signal Recovery

VIEW 12 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Sparse Solutions in Optimal Control of PDEs with Uncertain Parameters: The Linear Case

  • SIAM J. Control and Optimization
  • 2019
VIEW 5 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Compassionately Conservative Balanced Cuts for Image Segmentation

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • 2018
VIEW 5 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

P-Tensor Product in Compressed Sensing

  • IEEE Internet of Things Journal
  • 2018
VIEW 4 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

Total Variation Iterative Linear Expansion of Thresholds with Applications in CT

  • 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2018
VIEW 4 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Network Latency Estimation for Personal Devices: A Matrix Completion Approach

  • IEEE/ACM Transactions on Networking
  • 2017
VIEW 7 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Ray space analysis with sparse recovery

  • 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
  • 2017
VIEW 6 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2009
2019

CITATION STATISTICS

  • 72 Highly Influenced Citations

  • Averaged 76 Citations per year over the last 3 years

References

Publications referenced by this paper.
SHOWING 1-10 OF 32 REFERENCES

Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

  • IEEE Transactions on Information Theory
  • 2006
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Decoding by linear programming

  • IEEE Transactions on Information Theory
  • 2005
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Exact Reconstruction of Sparse Signals via Nonconvex Minimization

  • IEEE Signal Processing Letters
  • 2007
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Lecture notes of the IMA course on Compressed Sensing

E. J. Candès
  • 2007
VIEW 1 EXCERPT

Similar Papers

Loading similar papers…