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
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
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
Source localization using a sparse representation framework to achieve superresolution
Multidimensional Systems and Signal Processing
Performance analysis for sparse support recovery
IEEE Transactions on Information Theory
SIAM Journal on Scientific Computing
Column subset selection via sparse approximation of SVD
Theoretical Computer Science
Pose-robust face recognition via sparse representation
Pattern Recognition
Efficient feedback scheme based on compressed sensing in MIMO wireless networks
Computers and Electrical Engineering
Hi-index | 36.06 |
The sparse representation of a multiple-measurement vector (MMV) is a relatively new problem in sparse representation. Efficient methods have been proposed. Although many theoretical results that are available in a simple case-single-measurement vector (SMV)-the theoretical analysis regarding MMV is lacking. In this paper, some known results of SMV are generalized to MMV. Some of these new results take advantages of additional information in the formulation of MMV. We consider the uniqueness under both an lscr0-norm-like criterion and an lscr1-norm-like criterion. The consequent equivalence between the lscr0-norm approach and the lscr1-norm approach indicates a computationally efficient way of finding the sparsest representation in a redundant dictionary. For greedy algorithms, it is proven that under certain conditions, orthogonal matching pursuit (OMP) can find the sparsest representation of an MMV with computational efficiency, just like in SMV. Simulations show that the predictions made by the proved theorems tend to be very conservative; this is consistent with some recent advances in probabilistic analysis based on random matrix theory. The connections will be discussed