PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning for larger datasets with the Gaussian process latent variable model
Neil Lawrence
In: AISTATS 2007, 21-24 Mar 2007, San Juan, Puerto Rico.


In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GP-LVM). We review three techniques and discuss how they may be implemented in the context of the GP-LVM. Each approach is then implemented on a well known benchmark data set and compared with earlier attempts to sparsify the model.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3813
Deposited By:Neil Lawrence
Deposited On:25 February 2008