Learning for larger datasets with the Gaussian process latent variable model
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.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Neil Lawrence|
|Deposited On:||25 February 2008|