Algorithms for simultaneous sparse approximation: part I: Greedy pursuit
Signal Processing - Sparse approximations in signal and image processing
Algorithms for simultaneous sparse approximation: part II: Convex relaxation
Signal Processing - Sparse approximations in signal and image processing
Digital Signal Processing
CG-M-FOCUSS and Its Application to Distributed Compressed Sensing
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Compressive light transport sensing
ACM Transactions on Graphics (TOG)
Morphological Diversity and Sparsity for Multichannel Data Restoration
Journal of Mathematical Imaging and Vision
K-hyperline clustering learning for sparse component analysis
Signal Processing
Review of user parameter-free robust adaptive beamforming algorithms
Digital Signal Processing
IEEE Transactions on Signal Processing
Improved FOCUSS method with conjugate gradient iterations
IEEE Transactions on Signal Processing
Blind multiband signal reconstruction: compressed sensing for analog signals
IEEE Transactions on Signal Processing
Compressed sensing of analog signals in shift-invariant spaces
IEEE Transactions on Signal Processing
On the reconstruction of block-sparse signals with an optimal number of measurements
IEEE Transactions on Signal Processing
Dictionary learning for sparse approximations with the majorization method
IEEE Transactions on Signal Processing
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
Compressed sensing of time-varying signals
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
A neural network pruning approach based on compressive sampling
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Robust recovery of signals from a structured union of subspaces
IEEE Transactions on Information Theory
Uncertainty relations for shift-invariant analog signals
IEEE Transactions on Information Theory
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Block-sparse signals: uncertainty relations and efficient recovery
IEEE Transactions on Signal Processing
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
Direction-of-arrival estimation using a mixed l2,0norm approximation
IEEE Transactions on Signal Processing
Performance analysis for sparse support recovery
IEEE Transactions on Information Theory
Regularized sampling of multiband signals
IEEE Transactions on Signal Processing
SIAM Journal on Scientific Computing
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Sparse representations and sphere decoding for array signal processing
Digital Signal Processing
Compressive sensing based sub-mm accuracy UWB positioning systems: A space-time approach
Digital Signal Processing
Sparse Doppler-only snapshot imaging for space debris
Signal Processing
Efficient feedback scheme based on compressed sensing in MIMO wireless networks
Computers and Electrical Engineering
Block-sparse recovery via redundant block OMP
Signal Processing
| Hi-index | 36.07 |
We address the problem of finding sparse solutions to an underdetermined system of equations when there are multiple measurement vectors having the same, but unknown, sparsity structure. The single measurement sparse solution problem has been extensively studied in the past. Although known to be NP-hard, many single-measurement suboptimal algorithms have been formulated that have found utility in many different applications. Here, we consider in depth the extension of two classes of algorithms-Matching Pursuit (MP) and FOCal Underdetermined System Solver (FOCUSS)-to the multiple measurement case so that they may be used in applications such as neuromagnetic imaging, where multiple measurement vectors are available, and solutions with a common sparsity structure must be computed. Cost functions appropriate to the multiple measurement problem are developed, and algorithms are derived based on their minimization. A simulation study is conducted on a test-case dictionary to show how the utilization of more than one measurement vector improves the performance of the MP and FOCUSS classes of algorithm, and their performances are compared.