Abstract
Gaussian Processes (GPs) are powerful tools for
machine learning which have been applied to both
classification and regression. The mixture models of GPs were later proposed to further improve
GPs for data modeling. However, these models are formulated for regression problems. In
this work, we propose a new Mixture of Gaussian Processes for Classification (MGPC). Instead
of the Gaussian likelihood for regression, MGPC
employs the logistic function as likelihood to obtain the class probabilities, which is suitable for
classification problems. The posterior distribution
of latent variables is approximated through variational inference. The hyperparameters are optimized through the variational EM method and a
greedy algorithm. Experiments are performed on
multiple real-world datasets which show improvements over five widely used methods on predictive performance. The results also indicate that for
classification MGPC is significantly better than the
regression model with mixtures of GPs, different
from the existing consensus that their single model
counterparts are comparable