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Nonlinear Independent Component Analysis Using Ensemble Learning: Theory

Harri Valpola

Helsinki University of Technology, Neural Networks Research Centre
P.O.Box 5400, FIN-02015 HUT, Espoo, Finland
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A nonlinear version of independent component analysis is presented. The mapping from sources to observations is modelled by a multi-layer perceptron network and the distributions of sources are modelled by mixtures of Gaussians. The posterior probability of all the unknown parameters is estimated by ensemble learning. In this paper, we present the theory of the method, and in a companion paper experimental results.


Harri Valpola