Online Sparse Multi-Output Gaussian Process Regression and Learning

This paper proposes an approach for online training of a sparse multi-output Gaussian process (GP) model using sequentially obtained data. The considered model combines linearly multiple latent sparse GPs to produce correlated output variables. Each latent GP has its own set of inducing points to achieve sparsity. We show that given the model hyperparameters, the posterior over the inducing points is Gaussian under Gaussian noise since they are linearly related to the model outputs. However, the inducing points from different latent GPs would become correlated, leading to a full covariance matrix cumbersome to handle. Variational inference is thus applied and an approximate regression technique is obtained, with which the posteriors over different inducing point sets can always factorize. As the model outputs are non-linearly dependent on the hyperparameters, a novel marginalized particle filer (MPF)-based algorithm is proposed for the online inference of the inducing point values and hyperparameters. The approximate regression technique is incorporated in the MPF and its distributed realization is presented. Algorithm validation using synthetic and real data is conducted, and promising results are obtained.