title: Learning curves for mutual information maximization.
abstract: An unsupervised learning procedure based on maximizing the mutual information between the outputs of two networks receiving different but statistically dependent inputs is analyzed [S. Becker and G. Hinton, Nature (London) 355, 161 (1992)]. For a generic data model, I show that in the large sample limit the structure in the data is recognized by mutual information maximization. For a more restricted model, where the networks are similar to perceptrons, I calculate the learning curves for zero-temperature Gibbs learning. These show that convergence can be rather slow, and a way of regularizing the procedure is considered.
authors.affiliation: Institut für Theoretische Physik, Universität Würzburg, Am Hubland, D-97074 Würzburg, Germany.
authors.forename: R
authors.initials: R
authors.lastname: Urbanczik