PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6518
Deposited By:Finale Doshi
Deposited On:08 March 2010