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12

ICML
2007
IEEE

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Hierarchical Gaussian process latent variable models

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Hierarchical Gaussian process latent variable models

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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.

Neil D. Lawrence, Andrew J. Moore

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Gaussian Process Hierarchies | ICML 2007 | Latent Variable Model | Machine Learning | Simple Dynamical Model |

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Added	17 Nov 2009
Updated	17 Nov 2009
Type	Conference
Year	2007
Where	ICML
Authors	Neil D. Lawrence, Andrew J. Moore

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