Variational Gaussian mixtures for blind source detection
Nikolaos Nasios, Adrian G. Borş · 2004
Bayesian algorithms have lately been used in a large variety of applications. This paper proposes a new methodology for hyperparameter initialization in the Variational Bayes (VB) algorithm. We employ a dual expectation-maximization (EM) algorithm as the initialization stage in the VB-based learning. In the first stage, the EM algorithm is used on the given data set while the second EM algorithm is applied on distributions of parameters resulted from several runs of the first stage EM. The graphical model case study considered in this paper consists of a mixture of Gaussians. Appropriate conjugate prior distributions are considered for modelling the parameters. The proposed methodology is applied on blind source separation of modulated signals.