Sparse Convolved Gaussian Processes for Multi-output Regression
Mauricio Alvarez and Neil Lawrence
In: Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS), 8-11 Dec 2008, Vancouver, Canada.
We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to establish dependencies between output variables, where each
latent function is represented as a GP. Based on these latent functions, we establish an approximation scheme using a conditional independence assumption between the output processes, leading to an approximation of the full covariance which is determined by the locations at which the latent functions are evaluated. We show results of the proposed methodology for synthetic data and real world applications on pollution prediction and a sensor network.