Variational Learning of Inducing Variables in Sparse Gaussian Processes
In: Twelfth International Conference on Artificial Intelligence and Statistics, April 16-18, 2009, Clearwater Beach, Florida USA.
Sparse Gaussian process methods that use inducing
variables require the selection of the inducing
inputs and the kernel hyperparameters. We
introduce a variational formulation for sparse approximations that jointly
infers the inducing inputs and the kernel hyperparameters by
maximizing a lower bound of the true log marginal likelihood. The key property of this formulation is that the inducing inputs are defined to be variational parameters which are selected by minimizing the Kullback-Leibler divergence between the variational distribution and the exact posterior
distribution over the latent function values. We apply this technique to regression and we compare it with other approaches in the literature.