Hierarchical Gaussian Pro cess Latent Variable Mo dels
Neil D. Lawrence - School of Computer Science, University of Manchester, U.K.
Andrew J. Moore - Department of Computer Science, University of Sheffield, U.K
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.