The Annealing Sparse Bayesian Learning Algorithm
This work addresses a specific bottleneck in sparse Bayesian learning for signal processing or machine learning applications, representing an incremental improvement over existing methods.
The paper tackles the problem of noise variance learning in fast marginalized Sparse Bayesian Learning algorithms by proposing a two-level hierarchical Bayesian model with an annealing schedule, resulting in improved performance such as NMSE and F-measure, producing sparse solutions under moderate SNR scenarios while maintaining low computational load.
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.