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Connecting geometric independent component analysis to unsupervised learning algorithms

Theis, Fabian J. ; Gruber, Peter ; Puntonet, Carlos G. ; Lang, Elmar



Abstract

The goal of independent component analysis (ICA) lies in transforming a mixed random vector in order to render it as independent as possible. This paper shows how to use adaptive learning and clustering algorithms to approximate mixture space densities thus learning the mixing model. Here, a linear square-model is assumed, and as learning algorithm either a self-organizing map (SOM) or a neural ...

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