Górriz , J. M. and Puntonet, Carlos G. and Rojas, F. and Martin, R. and Hornillo, S. and Lang, Elmar (2006) Optimizing blind source separation with guided genetic algorithms. Neurocomputing 69 (13-15), pp. 1442-1457.
Full text not available from this repository.
This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its application to the problem of blind source separation (BSS) on post-nonlinear mixtures. The paper also includes a formal proof on the convergence of the proposed algorithm using guiding operators, a new concept in the GA scenario. This approach is very useful in many fields such as forecasting indexes in financial stock markets, where the search for independent components is the major task to include exogenous information into the learning machine; or biomedical applications which usually use a high number of input signals. The guiding GA (GGA) presented in this work, is able to extract IC with faster rate than the previous ICA algorithms, as input space dimension increases. It shows significant accuracy and robustness than the previous approaches in any case. In addition, we present a simple though effective contrast function which evaluates individuals of each population (candidate solutions) based (a) on estimating the probability densities of the outputs through histogram approximation and (b) evaluating higher-order statistics of the outputs.
|Institutions:||Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang|
|Keywords:||Independent component analysis (ICA); Genetic algorithm (GA); Guiding genetic algorithm (GGA); Higher order statistics (HOS); Mutual information|
|Subjects:||500 Science > 570 Life sciences|
|Created at the University of Regensburg:||Unknown|
|Deposited On:||05 Oct 2010 06:32|
|Last Modified:||05 Oct 2010 06:32|