Name
Abstract | ||
|---|---|---|
The aim of this paper is to develop strategies to estimate the sparsity degree of a signal from compressive projections, without the burden of recovery. We consider both the noise-free and the noisy settings, and we show how to extend the proposed framework to the case of non-exactly sparse signals. The proposed method employs γ-sparsified random matrices and is based on a maximum likelihood (ML) approach, exploiting the property that the acquired measurements are distributed according to a mixture model whose parameters depend on the signal sparsity. In the presence of noise, given the complexity of ML estimation, the probability model is approximated with a two-component Gaussian mixture (2-GMM), which can be easily learned via expectation-maximization. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s13634-018-0578-0 | EURASIP Journal on Advances in Signal Processing |
Keywords | Field | DocType |
Sparsity recovery,Compressed sensing,High-dimensional statistical inference,Gaussian mixture models,Maximum likelihood,Sparse random matrices | Computer vision,Probability model,Computer science,Algorithm,Maximum likelihood,Gaussian,Artificial intelligence,Compressed sensing,Mixture model,Random matrix | Journal |
Volume | Issue | ISSN |
2018 | 1 | 1687-6180 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chiara Ravazzi | 1 | 114 | 13.23 |
| Sophie M. Fosson | 2 | 44 | 8.96 |
| Tiziano Bianchi | 3 | 1003 | 62.55 |
| Enrico Magli | 4 | 1319 | 114.81 |