Dirichlet process mixture models for finding shared structure between two related data sets
Gayle Leen, Colin Fyfe · International Conference on Artificial Intelligence · 2008
A nonparametric Bayesian approach is used for the problem of learning from two related data sets. We model the shared structure between two data sets using a Dirichlet process mixture model of probabilistic canonical correlation analysers, which allows the flexibility of the mappings from shared feature to data spaces to be automatically determined from the data.