Paper Info
Title
Analysis of contrast functions in a genetic algorithm for post-nonlinear blind source separation
Abstract
The task of recovering a set of unknown sources from another set of mixtures directly observable and little more information about the way they were mixed is called the blind source separation problem. If the assumption in order to obtain the original sources is their statistical independence, then ICA (Independent Component Analysis) maybe the technique to recover the signals. In this contribution, we propose and analyze three evaluation functions (contrast functions in Independent Component Analysis terminology) for the use in a genetic algorithm (PNL-GABSS, Post-NonLinear Genetic Algorithm for Blind Source Separation) which solves source separation in nonlinear mixtures, assuming the post-nonlinear mixture model. A thorough analysis of the performance of the chosen contrast functions is made by means of ANOVA (Analysis of Variance), showing the validity of the three approaches.
Year
Venue
Keywords
2005
ESANN
mixture model,blind source separation,genetic algorithm,evaluation function,statistical independence,independent component analysis
Field
DocType
Citations 
Nonlinear system,Observable,Pattern recognition,Computer science,Independent component analysis,Artificial intelligence,Blind signal separation,Source separation,Genetic algorithm,Independence (probability theory),Mixture model
Conference
0
PageRank 
References 
Authors
0.34
5
3
Name
Order
Citations
PageRank
Fernando Rojas Ruiz1347.37
Carlos García Puntonet210725.86
I. Rojas31750143.09