Learning for Larger Datasets with the Gaussian Process Latent Variable Model
Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics, Omnipress :243-250, 2007.
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.