Adaptive variational sparse Bayesian estimation
Konstantinos E. Themelis, Athanasios A. Rontogiannis, Konstantinos Koutroumbas · 2014
This paper presents an online version of the widely used sparse Bayesian learning (SBL) algorithm. Exploiting the variational Bayes framework, an efficient online SBL algorithm is constructed, that acts as a fully automatic learning method for the adaptive estimation of sparse time-varying signals. The new method is based on second order statistics and comprises a simple, automated sparsity-imposing mechanism, different from that of other known schemes. The effectiveness of the proposed online Bayesian algorithm is illustrated using experimental results conducted on synthetic data. These results show that the proposed scheme achieves faster initial convergence and superior estimation performance compared to other related state-of-the-art schemes.