Learning a Hierarchical Belief Network of Independent Factor Analyzers

The model parameters are learned in an unsupervised manner by maximizing the likelihood that these data are generated by the model. A multilayer belief network is a realization of such a model. Many belief networks have been proposed that are composed of binary units. The hidden units in such networks represent latent variables that explain different features of the data, and whose relation to the ·Current address: Gatsby Computational Neuroscience Unit, University College London, 17 Queen Square, London WC1N 3AR, U.K. 362 H. Attias data is highly nonlinear. However, for tasks such as object and speech recognition which produce real-valued data, the models provided by binary networks are often inadequate.