Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Neural Computation
Convex Optimization
A fast approach for overcomplete sparse decomposition based on smoothed l0 norm
IEEE Transactions on Signal Processing
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
Fast sparse representation based on smoothed lo norm
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
Uncertainty principles and ideal atomic decomposition
IEEE Transactions on Information Theory
Decoding by linear programming
IEEE Transactions on Information Theory
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Direction-of-arrival estimation using a mixed l2,0norm approximation
IEEE Transactions on Signal Processing
| Hi-index | 35.69 |
lo norm based algorithms have numerous potential applications where a sparse signal is recovered from a small number of measurements. The direct l0 norm optimization problem is NP-hard. In this paper we work with the the smoothed l0 (SL0) approximation algorithm for sparse representation. We give an upper bound on the run-time estimation error. This upper bound is tighter than the previously known bound. Subsequently, we develop a reliable stopping criterion. This criterion is helpful in avoiding the problems due to the underlying discontinuities of the l0 cost function. Furthermore, we propose an alternative optimization strategy, which results in a Newton like algorithm.