Using Mixture of Gaussians to Compare Approaches to Signal Separation 
Kaare Brandt Petersen

Abstract  Signal separation techniques come in countless different types and
variants. Often many of these belong to certain subset of
techniques such as Independent Component Analysis (ICA), but even
within such a subset it can be hard or impossible to compare the
different approaches.
In this paper is an example of how such different approaches to
separation can be compared using Mixtures of Gaussians as a prior
distribution. This not only illuminates some interesting
properties of Maximum Likelihood and Energy Based Models, but is
also an example of how Mixtures of Gaussians can serve as a both
flexible and analytically tractable family of distributions. 
Keywords  ICA, Overcomplete, Mixture of Gaussians, Energy Based Models 
Type  Conference paper [Without referee] 
Conference  DSAGM 
Year  2004 Month August 
Electronic version(s)  [pdf] 
BibTeX data  [bibtex] 
IMM Group(s)  Intelligent Signal Processing 