An experimental comparison of neural algorithms for independent component analysis and blind separation.
Int J Neural Syst
; 9(2): 99-114, 1999 Apr.
Article
em En
| MEDLINE
| ID: mdl-10529083
ABSTRACT
In this paper, we compare the performance of five prominent neural or adaptive algorithms designed for Independent Component Analysis (ICA) and blind source separation (BSS). In the first part of the study, we use artificial data for comparing the accuracy, convergence speed, computational load, and other relevant properties of the algorithms. In the second part, the algorithms are applied to three different real-world data sets. The task is either blind source separation or finding interesting directions in the data for visualisation purposes. We develop criteria for selecting the most meaningful basis vectors of ICA and measuring the quality of the results. The comparison reveals characteristic differences between the studied ICA algorithms. The most important conclusions of our comparison are robustness of the ICA algorithms with respect to modest modeling imperfections, and the superiority of fixed-point algorithms with respect to the computational load.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Redes Neurais de Computação
Tipo de estudo:
Prognostic_studies
Limite:
Animals
/
Female
/
Humans
/
Male
Idioma:
En
Revista:
Int J Neural Syst
Assunto da revista:
ENGENHARIA BIOMEDICA
/
INFORMATICA MEDICA
Ano de publicação:
1999
Tipo de documento:
Article
País de afiliação:
Finlândia