PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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


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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:6238
Deposited By:Zoubin Ghahramani
Deposited On:08 March 2010