Many linear ICA techniques are based on minimizing a non-linear contrast function and many of them use a hyperbolic tangent (tanh) as their built-in nonlinearity. In this paper we propose two rational functions to replace the tanh and other popular functions that are tailored for separating supergaussian (long-tailed) sources. The advantage of the rational function is two-fold. First, the rational function requires a significantly lower computational complexity than tanh, e.g. nine times lower. As a result, algorithms using the rational functions are typically twice faster than algorithms with tanh. Second, it can be shown that a suitable selection of the rational function allows to achieve a better performance of the separation in certain scenarios. This improvement might be systematic, if the rational nonlinearities are selected adaptively to data. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Tichavský, P., Koldovský, Z., & Oja, E. (2007). Speed and accuracy enhancement of linear ICA techniques using rational nonlinear functions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4666 LNCS, pp. 285–292). Springer Verlag. https://doi.org/10.1007/978-3-540-74494-8_36
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