The Annealing Sparse Bayesian Learning Algorithm

TL;DR

This paper introduces an annealing hierarchical Bayesian algorithm that enhances sparse signal recovery, improves performance metrics, and maintains low computational complexity compared to existing Sparse Bayesian Learning methods.

Contribution

It proposes a novel annealing schedule for hierarchical Bayesian models that restores noise variance learning and enhances sparsity and accuracy in SBL algorithms.

Findings

Significant improvement in NMSE and F-measure with annealing.

Produces highly sparse solutions at moderate SNRs.

Outperforms most existing SBL algorithms with low computational load.

Abstract

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.