Unsupervised Variational Bayesian Learning of Nonlinear Models

@inproceedings{Honkela2004UnsupervisedVB,
  title={Unsupervised Variational Bayesian Learning of Nonlinear Models},
  author={Antti Honkela and Harri Valpola},
  booktitle={NIPS},
  year={2004}
}
In this paper we present a framework for using multi-layer perceptron (MLP) networks in nonlinear generative models trained by variational Bayesian learning. The nonlinearity is handled by linearizing it using a Gauss–Hermite quadrature at the hidden neurons. This yields an accurate approximation for cases of large posterior variance. The method can be used to derive nonlinear counterparts for linear algorithms such as factor analysis, independent component/factor analysis and state-space… CONTINUE READING

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