Abstract
This paper proposes a novel Independent Component Analysis algorithm based on the use of a genetic algorithm intended for its application to the problem of blind source separation on post-nonlinear mixtures. We present a simple though effective contrast function which evaluates individuals of each population (candidate solutions) based on estimating the probability densities of the outputs through histogram approximation. Although more sophisticate methods for probability density function approximation exist, such as kernel-based methods or k-nearest-neighbor estimation, the histogram presents the advantage of its simplicity and easy calculation if an appropriate number of samples is available.
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Rojas Ruiz, F., Puntonet, C.G., Rojas Ruiz, I., Rodríguez-Álvarez, M., Górriz, J.M. (2004). Plugging an Histogram-Based Contrast Function on a Genetic Algorithm for Solving PostNonLinear-BSS. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_96
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DOI: https://doi.org/10.1007/978-3-540-30110-3_96
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