Double sparsity: learning sparse dictionaries for sparse signal approximation
IEEE Transactions on Signal Processing
Sparse Legendre expansions via l1-minimization
Journal of Approximation Theory
| Hi-index | 35.69 |
This paper introduces the concept of sensing dictionaries. It presents an alteration of greedy algorithms like thresholding or (orthogonal) matching pursuit which improves their performance in finding sparse signal representations in redundant dictionaries while maintaining the same complexity. These algorithms can be split into a sensing and a reconstruction step, and the former will fail to identify correct atoms if the cumulative coherence of the dictionary is too high. We thus modify the sensing step by introducing a special sensing dictionary. The correct selection of components is then determined by the cross cumulative coherence which can be considerably lower than the cumulative coherence. We characterize the optimal sensing matrix and develop a constructive method to approximate it. Finally, we compare the performance of thresholding and OMP using the original and modified algorithms.