Name
Abstract | ||
|---|---|---|
In this paper, we develop a Bayesian evidence maximization framework to solve the sparse non-negative least squares (S-NNLS) problem. We introduce a family of probability densities referred to as the Rectified Gaussian Scale Mixture (R- GSM) to model the sparsity enforcing prior distribution for the solution. The R-GSM prior encompasses a variety of heavy-tailed densities such as the rectified Laplacian and rectified Student- t distributions with a proper choice of the mixing density. We utilize the hierarchical representation induced by the R-GSM prior and develop an evidence maximization framework based on the Expectation-Maximization (EM) algorithm. Using the EM based method, we estimate the hyper-parameters and obtain a point estimate for the solution. We refer to the proposed method as rectified sparse Bayesian learning (R-SBL). We provide four R- SBL variants that offer a range of options for computational complexity and the quality of the E-step computation. These methods include the Markov chain Monte Carlo EM, linear minimum mean-square-error estimation, approximate message passing and a diagonal approximation. Using numerical experiments, we show that the proposed R-SBL method outperforms existing S-NNLS solvers in terms of both signal and support recovery performance, and is also very robust against the structure of the design matrix. |
Year | Venue | DocType |
|---|---|---|
2018 | IEEE Trans. Signal Processing | Journal |
Volume | Issue | Citations |
abs/1601.06207 | 12 | 2 |
PageRank | References | Authors |
0.36 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alican Nalci | 1 | 48 | 3.23 |
| Igor Fedorov | 2 | 12 | 6.07 |
| Bhaskar Rao | 3 | 4037 | 449.28 |