An efficient multivariate generalized Gaussian distribution estimator: Application to IVA
Zois Boukouvalas, Geng-Shen Fu, Tülay Adalı · 2015
Due to its simple parametric form, multivariate generalized Gaussian distribution (MGGD) has been widely used for modeling vector-valued signals. Therefore, efficient estimation of its parameters is of significant interest for a number of applications. Independent vector analysis (IVA) is a generalization of independent component analysis (ICA) that makes full use of the statistical dependence across multiple datasets to achieve source separation, and can take both second and higher-order statistics into account. MGGD provides an effective model for IVA as well as for modeling the latent multivariate variables-sources-and the performance of the IVA algorithm highly depends on the estimation of the source parameters. In this paper, we propose an efficient estimation technique based on the Fisher scoring (FS) and demonstrate its successful application to IVA. We quantify the performance of MGGD parameter estimation using FS and further verify the effectiveness of the new IVA algorithm using simulations.