PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Dependent Indian Buffet Processes
Sinead Williamson, Peter Orbanz and Zoubin Ghahramani
In: AISTATS 2010, 13-15 May 2010, Sardinia, Italy.


Latent variable models represent hidden structure in observational data. To account for the distribution of the observational data changing over time, space or some other covariate, we need generalizations of latent variable models that explicitly capture this dependency on the covariate. A variety of such generalizations has been proposed for latent variable models based on the Dirichlet process. We address dependency on covariates in binary latent feature models, by introducing a dependent Indian Buffet Process. The model generates a binary random matrix with an unbounded number of columns for each value of the covariate. Evolution of the binary matrices over the covariate set is controlled by a hierarchical Gaussian process model. The choice of covariance functions controls the dependence structure and exchangeability properties of the model. We derive a Markov Chain Monte Carlo sampling algorithm for Bayesian inference, and provide experiments on both synthetic and real-world data. The experimental results show that explicit modeling of dependencies significantly improves accuracy of predictions.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6282
Deposited By:Peter Orbanz
Deposited On:08 March 2010