Correlated non-parametric latent feature models
Finale Doshi and Zoubin Ghahramani
In: UAI 2009, 18-21 JUN 2009, Montreal, Quebec.

Abstract

We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is un- known, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many realworld problems. We introduce a framework for correlated non- parametric feature models, generalising the IBP. We use this framework to generate sev- eral specific models and demonstrate appli- cations on realworld datasets.

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