Matrix computations (3rd ed.)
Approximation of functions over redundant dictionaries using coherence
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Journal of Complexity
Algorithms for simultaneous sparse approximation: part II: Convex relaxation
Signal Processing - Sparse approximations in signal and image processing
Sparse solutions to linear inverse problems with multiple measurement vectors
IEEE Transactions on Signal Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Algorithms for simultaneous sparse approximation: part II: Convex relaxation
Signal Processing - Sparse approximations in signal and image processing
Sparse approximations for high fidelity compression of network traffic data
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
Compressive light transport sensing
ACM Transactions on Graphics (TOG)
Morphological Diversity and Sparsity for Multichannel Data Restoration
Journal of Mathematical Imaging and Vision
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
IEEE Transactions on Signal Processing
Blind multiband signal reconstruction: compressed sensing for analog signals
IEEE Transactions on Signal Processing
On the reconstruction of block-sparse signals with an optimal number of measurements
IEEE Transactions on Signal Processing
Compressive-projection principal component analysis
IEEE Transactions on Image Processing
3D face recognition with sparse spherical representations
Pattern Recognition
Sampling theorems for signals from the union of finite-dimensional linear subspaces
IEEE Transactions on Information Theory
Block sparsity and sampling over a union of subspaces
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
l2/l1-optimization and its strong thresholds in approximately block-sparse compressed sensing
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Robust recovery of signals from a structured union of subspaces
IEEE Transactions on Information Theory
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Model-based compressive sensing for signal ensembles
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Learning sparse representation using iterative subspace identification
IEEE Transactions on Signal Processing
Model-based compressive sensing
IEEE Transactions on Information Theory
Average case analysis of multichannel sparse recovery using convex relaxation
IEEE Transactions on Information Theory
Theoretical and empirical results for recovery from multiple measurements
IEEE Transactions on Information Theory
Minimizing nonconvex functions for sparse vector reconstruction
IEEE Transactions on Signal Processing
Performance analysis for sparse support recovery
IEEE Transactions on Information Theory
EURASIP Journal on Advances in Signal Processing - Special issue on applications of time-frequency signal processing in wireless communications and bioengineering
Exact optimization for the l1-Compressive Sensing problem using a modified Dantzig-Wolfe method
Theoretical Computer Science
Two-dimensional random projection
Signal Processing
SIAM Journal on Scientific Computing
Efficient Sensing Topology Management for Spatial Monitoring with Sensor Networks
Journal of Signal Processing Systems
A Bayesian Lasso via reversible-jump MCMC
Signal Processing
Pixel-level image fusion with simultaneous orthogonal matching pursuit
Information Fusion
Automated identification of thoracolumbar vertebrae using orthogonal matching pursuit
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Joint dynamic sparse representation for multi-view face recognition
Pattern Recognition
Toward efficient spatial variation decomposition via sparse regression
Proceedings of the International Conference on Computer-Aided Design
Column subset selection via sparse approximation of SVD
Theoretical Computer Science
Optimization with Sparsity-Inducing Penalties
Foundations and Trends® in Machine Learning
Compressive sensing based sub-mm accuracy UWB positioning systems: A space-time approach
Digital Signal Processing
Sparse methods for biomedical data
ACM SIGKDD Explorations Newsletter
Design of non-linear discriminative dictionaries for image classification
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Efficient feedback scheme based on compressed sensing in MIMO wireless networks
Computers and Electrical Engineering
Superpixel-wise semi-supervised structural sparse coding classifier for image segmentation
Engineering Applications of Artificial Intelligence
An SDP approach for ℓ0-minimization: Application to ARX model segmentation
Automatica (Journal of IFAC)
Automated identification of thoracolumbar vertebrae using orthogonal matching pursuit
Machine Vision and Applications
Block-sparse recovery via redundant block OMP
Signal Processing
One-shot learning gesture recognition from RGB-D data using bag of features
The Journal of Machine Learning Research
A note on sparse least-squares regression
Information Processing Letters
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A simultaneous sparse approximation problem requests a good approximation of several input signals at once using different linear combinations of the same elementary signals. At the same time, the problem balances the error in approximation against the total number of elementary signals that participate. These elementary signals typically model coherent structures in the input signals, and they are chosen from a large, linearly dependent collection.The first part of this paper proposes a greedy pursuit algorithm, called simultaneous orthogonal matching pursuit (S-OMP), for simultaneous sparse approximation. Then it presents some numerical experiments that demonstrate how a sparse model for the input signals can be identified more reliably given several input signals. Afterward, the paper proves that the S-OMP algorithm can compute provably good solutions to several simultaneous sparse approximation problems.The second part of the paper develops another algorithmic approach called convex relaxation, and it provides theoretical results on the performance of convex relaxation for simultaneous sparse approximation.