Correlated non-parametric latent feature models

Authors:
Finale Doshi-Velez;Zoubin Ghahramani
Affiliations:
Cambridge University, Cambridge, UK;Cambridge University, Cambridge, UK
Venue:
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Year:
2009

Citing 4
Cited 1

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The Indian Buffet Process: An Introduction and Review

The Journal of Machine Learning Research

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Abstract

We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, 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 several specific models and demonstrate applications on realworld datasets.