Multi-layer perceptrons as nonlinear generative models for unsupervised learning: a Bayesian treatment

摘要

In this paper, multi-layer perceptrons are used as nonlinear generative models. The problem of indeterminacy of the models is resolved using a recently developed Bayesian method called ensemble learning. Using a Bayesian approach, models can becompared according to their probabilities. In simulations with artificial data, the network is able to find the underlying causes of the observations despite the strong nonlinearities of the data.