Name
Abstract | ||
|---|---|---|
In this paper, the use of the Generalized Beta Mixture (GBM) and Horseshoe distributions as priors in the Bayesian Compressive Sensing framework is proposed. The distributions are considered in a two-layer hierarchical model, making the corresponding inference problem amenable to Expectation Maximization (EM). We present an explicit, algebraic EM-update rule for the models, yielding two fast and experimentally validated algorithms for signal recovery. Experimental results show that our algorithms outperform state-of-the-art methods on a wide range of sparsity levels and amplitudes in terms of reconstruction accuracy, convergence rate and sparsity. The largest improvement can be observed for sparse signals with high amplitudes. |
Year | Venue | Field |
|---|---|---|
2014 | CoRR | Algebraic number,Artificial intelligence,Rate of convergence,Beta (finance),Amplitude,Hierarchical database model,Mathematical optimization,Inference,Expectation–maximization algorithm,Algorithm,Prior probability,Mathematics,Machine learning |
DocType | Volume | Citations |
Journal | abs/1411.2405 | 0 |
PageRank | References | Authors |
0.34 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zahra Sabetsarvestani | 1 | 0 | 0.34 |
| hamidreza amindavar | 2 | 215 | 36.34 |