Learning for Larger Datasets with the Gaussian Process Latent Variable Model
Neil D. Lawrence;
JMLR W&P 2:243-250, 2007.
In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GPLVM). 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.