Name
Title | ||
|---|---|---|
Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices. |
Abstract | ||
|---|---|---|
In this paper, we propose a method for estimating the sparsity of a signal from its noisy linear projections without recovering it. The method exploits the property that linear projections acquired using a sparse sensing matrix are distributed according to a mixture distribution whose parameters depend on the signal sparsity. Due to the complexity of the exact mixture model, we introduce an approximate two-component Gaussian mixture model whose parameters can be estimated via expectation-maximization techniques. We demonstrate that the above model is accurate in the large system limit for a proper choice of the sensing matrix sparsifying parameter. Moreover, experimental results demonstrate that the method is robust under different signal-to-noise ratios and outperforms existing sparsity estimation techniques. |
Year | Venue | Field |
|---|---|---|
2016 | ICASSP | Mixture distribution,Signal processing,Pattern recognition,Computer science,Matrix (mathematics),Software,Artificial intelligence,Mixture model,Compressed sensing,Sparse matrix,Random matrix |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
14 | 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 |