Paper Info
Title
Plugging an Histogram-Based Contrast Function on a Genetic Algorithm for Solving PostNonLinear-BSS
Abstract
This paper proposes a novel Independent Component Analysis algorithm based on the use of a genetic algorithm intended for its application to the problem of blind source separation on post-nonlinear mixtures. We present a simple though effective contrast function which evaluates individuals of each population (candidate solutions) based on estimating the probability densities of the outputs through histogram approximation. Although more sophisticate methods for probability density function approximation exist, such as kernel-based methods or k-nearest-neighbor estimation, the histogram presents the advantage of its simplicity and easy calculation if an appropriate number of samples is available.
Year
DOI
Venue
2004
10.1007/978-3-540-30110-3_96
ICA
Keywords
Field
DocType
k nearest neighbor,probability density,probability density function,independent component analysis,blind source separation,genetic algorithm
Population,Histogram,Computer science,Algorithm,Divergence (statistics),Independent component analysis,Blind signal separation,Probability density function,Source separation,Genetic algorithm
Conference
Citations 
PageRank 
References 
0
0.34
8
Authors
5
Name
Order
Citations
PageRank
Fernando Rojas Ruiz1347.37
Carlos García Puntonet210725.86
I. Rojas31750143.09
Manuel Rodríguez Álvarez4123.37
Juan Manuel Górriz Sáez5132.19