Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications. We introduce a new approximation for large-scale Gaussian processes, the Gaussian Process Random Field (GPRF), in which local GPs are coupled via pairwise potentials. The GPRF likelihood is a simple, tractable, and parallelizeable approximation to the full GP marginal likelihood, enabling latent variable modeling and hyperparameter selection on large datasets. We demonstrate its effectiveness on synthetic spatial data as well as a real-world application to seismic event location.
When the Markov model is fully connected, it is the Gaussian Process Random Field (GPRF) REF , which approximates the global GP by coupling local GPs via pairwise potential functions.
Gaussian Process Random Fields
ArXiv
ArXiv
Computer Science Mathematics