Correlated Non-Parametric Latent Feature Models
Finale Doshi and Zoubin Ghahramani
In: Conference on Uncertainty in Artificial Intelligence, 18-21 June 2009, Montreal, Canada.
We are often interested in explaining data through a set of hidden factors or features. To allow for an unknown number of such hidden features, one can use the IBP: a nonparametric latent feature model that does not bound the number of active features in a dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many real-world problems. We introduce a framework for correlated nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on real-world datasets.