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A comparison of neural ICA algorithms using real-world data

We compare the performance of five prominent neural or semi-neural algorithms designed for independent component analysis (ICA) using three different real-world data sets. The task is either to find interesting directions in the data for visualisation purposes or blind source separation. We develop criteria for selecting the most meaningful basis vectors of ICA or measuring the goodness of results. The comparison reveals characteristic differences between the studied neural ICA algorithms, complementing our previous results (1998) obtained for artificially generated data.