AN EXPERIMENTAL COMPARISON OF NEURAL ALGORITHMS FOR INDEPENDENT COMPONENT ANALYSIS AND BLIND SEPARATION
- 1 April 1999
- journal article
- research article
- Published by World Scientific Pub Co Pte Ltd in International Journal of Neural Systems
- Vol. 9 (2), 99-114
- https://doi.org/10.1142/s0129065799000101
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.Keywords
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