Hierarchical Gaussian process latent variable models

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.

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Neil D. Lawrence, Andrew J. Moore
• Keywords: Gaussian Process Hierarchies, ICML 2007, Latent Variable Model, Machine Learning, Simple Dynamical Model