PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Random function priors for exchangeable arrays with applications to graphs and relational data
James Lloyd, Peter Orbanz, Zoubin Ghahramani and Daniel Roy
In: Advances in Neural Information Processing Systems 2012, 3-8 Dec 2012, Lake Tahoe, USA.

Abstract

A fundamental problem in the analysis of structured relational data like graphs, networks, databases, and matrices is to extract a summary of the common structure underlying relations between individual entities. Relational data are typically encoded in the form of arrays; invariance to the ordering of rows and columns corresponds to exchangeable arrays. Results in probability theory due to Aldous, Hoover and Kallenberg show that exchangeable arrays can be represented in terms of a random measurable function which constitutes the natural model parameter in a Bayesian model. We obtain a flexible yet simple Bayesian nonparametric model by placing a Gaussian process prior on the parameter function. Efficient inference utilises elliptical slice sampling combined with a random sparse approximation to the Gaussian process. We demonstrate applications of the model to network data and clarify its relation to models in the literature, several of which emerge as special cases.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:9630
Deposited By:James Lloyd
Deposited On:08 December 2012