Variational Gaussian mixtures for blind source detection

被引：0

作者：

Nasios, N ^{[1
]}

Bors, AG ^{[1
]}

机构：

[1] Univ York, Dept Comp Sci, York YO10 5DD, N Yorkshire, England

来源：

2003 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-5, CONFERENCE PROCEEDINGS | 2003年

关键词：

Gaussian mixtures; Bayesian inference; variational learning; expectation-maximization algorithm;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：474 / 479

页数：6

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