A compact formulation for the l21 mixed-norm minimization problem

@article{Steffens2017ACF,
  title={A compact formulation for the l21 mixed-norm minimization problem},
  author={C. Steffens and M. Pesavento and M. Pfetsch},
  journal={2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2017},
  pages={4730-4734}
}
  • C. Steffens, M. Pesavento, M. Pfetsch
  • Published 2017
  • Mathematics, Computer Science
  • 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
We present an equivalent, compact reformulation of the ℓ2,1 mixed-norm minimization problem for joint sparse signal reconstruction from multiple measurement vectors (MMVs). The reformulation builds upon a compact parameterization, which models the row-norms of the sparse signal representation as parameters of interest, resulting in a significant reduction of the MMV problem size. Given the sparse vector of row-norms, the joint sparse signal can be computed from the MMVs in closed form. For the… Expand
29 Citations
A Compact Formulation for the $\ell _{2,1}$ Mixed-Norm Minimization Problem
  • 13
Distributed Computation for Sparse Optimization with Linear Equality Constraints
A parallel sparse regularization method for structured multilinear low-rank tensor decomposition
  • 1
Block- and Rank-Sparse Recovery for Direction Finding in Partly Calibrated Arrays
  • 13
  • PDF
Multiple Measurement Vectors Problem: A Decoupling Property and its Applications
  • 2
  • PDF
Recent Techniques for Regularization in Partial Differential Equations and Imaging
  • 1
  • PDF
Low-Complexity Massive MIMO Subspace Estimation and Tracking From Low-Dimensional Projections
  • 40
  • PDF
Gridless compressed sensing for fully augmentable arrays
  • 2
  • PDF
Low Complexity Beamspace Super Resolution for DOA Estimation of Linear Array
  • 1
  • PDF
...
1
2
3
...

References

SHOWING 1-10 OF 68 REFERENCES
A Compact Formulation for the $\ell _{2,1}$ Mixed-Norm Minimization Problem
  • 13
A sparse signal reconstruction perspective for source localization with sensor arrays
  • 1,858
  • Highly Influential
  • PDF
The null space property for sparse recovery from multiple measurement vectors
  • 47
  • PDF
Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm
  • 1,694
  • PDF
Exact Joint Sparse Frequency Recovery via Optimization Methods
  • Z. Yang, L. Xie
  • Mathematics, Computer Science
  • IEEE Transactions on Signal Processing
  • 2016
  • 146
  • PDF
Off-the-Grid Line Spectrum Denoising and Estimation With Multiple Measurement Vectors
  • 153
  • Highly Influential
  • PDF
Rank Awareness in Joint Sparse Recovery
  • 277
  • PDF
Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization
  • D. Donoho, Michael Elad
  • Computer Science, Medicine
  • Proceedings of the National Academy of Sciences of the United States of America
  • 2003
  • 2,697
  • PDF
Direction-of-Arrival Estimation Using a Mixed �
  • 107
Theoretical Results on Sparse Representations of Multiple-Measurement Vectors
  • J. Chen, X. Huo
  • Mathematics, Computer Science
  • IEEE Transactions on Signal Processing
  • 2006
  • 695
  • PDF
...
1
2
3
4
5
...