In this paper we propose a two-level hierarchical Bayesian model and an annealing schedule to re-enable the noise variance learning capability of the fast marginalized Sparse Bayesian Learning Algorithms. The performance such as NMSE and F-measure can be greatly improved due to the annealing technique. This algorithm tends to produce the most sparse solution under moderate SNR scenarios and can outperform most concurrent SBL algorithms while pertains small computational load.
@article{arxiv.1209.1033,
title = {The Annealing Sparse Bayesian Learning Algorithm},
author = {Benyuan Liu and Hongqi Fan and Zaiqi Lu and Qiang Fu},
journal= {arXiv preprint arXiv:1209.1033},
year = {2013}
}
Comments
The update equation in the annealing process was too empirical for practical usage. This paper need to be revised in order to be printed on the arxiv.org