PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Hierarchical Gaussian process latent variable models
Neil Lawrence and Andrew Moore
In: ICML 2007, 20-24 June 2007, Corvallis, Oregon, U.S.A..

Abstract

The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we extend the GP-LVM through hierarchies. A hierarchical model (such as a tree) allows us to express conditional independencies in the data as well as the manifold structure. We first introduce Gaussian process hierarchies through a simple dynamical model, we then extend the approach to a more complex hierarchy which is applied to the visualisation of human motion data sets.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3807
Deposited By:Neil Lawrence
Deposited On:25 February 2008