Hierarchical Bayesian modelling with Gaus-sian processes
We present a novel method for Gaussian process regression in a hierarchical Bayesian framework. The method is suited for cases where data from separate, but related problem scenarios is available. In the hierarchical framework, kernel matrices on a fixed set of input points (transduction) can be learned from data using a simple and efficient EM algorithm. This does not require a parametric form for the kernel function, thus problems that require, for example, nonstationary kernels, can be handled. We evaluate our approach as a recommendation engine for art images, where the proposed hierarchical Bayesian method leads to excellent prediction performance.