Blind Source Separation, Independent Component Analysis, and Pattern Classification — Connections and Synergies
Julian L. Center · AIP conference proceedings · 2004
By employing Bayesian methods and Mixture‐Of‐Gaussian (MOG) models, we derive a set of algorithms, based on the Expectation Maximization (EM) approach, that can be used for Blind Source Separation (BSS), Independent Component Analysis (ICA) and Pattern Classification (PC). All of these applications share a common generative model, which describes how each observation is produced. By constraining the model parameters in different ways, we can support each of these applications. ICA requires the most restricted model, BSS employs a less restricted model, and PC uses the least restricted model. Thus, in a sense, the PC model contains the BSS model, which in turn contains the ICA model. The relationships between these methods provide important synergies which can be exploited to both speed training and to extend the applicability of the methods.