Helsinki University of Technology
Neural Networks Research Centre
FIN-02015 HUT, FINLAND
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In this paper, a recently developed Bayesian method called ensemble learning is applied to independent component analysis (ICA).
Ensemble learning is a computationally efficient approximation for exact Bayesian analysis. In general, the posterior probability density function (pdf) is a complex high dimensional function whose exact treatment is difficult. In ensemble learning, the posterior pdf is approximated by a more simple function and Kullback-Leibler information is used as the criterion for minimising the misfit between the actual posterior pdf and its parametric approximation. In this paper, the posterior pdf is approximated by a diagonal Gaussian pdf.
According to the ICA-model used in this paper, the measurements are generated by a linear mapping from mutually independent source signals whose distributions are mixtures of Gaussians. The measurements are also assumed to have additive Gaussian noise with diagonal covariance.
The model structure and all parameters of the distributions are estimated from the data.