Sparse Multi-output Gaussian Processes
published: Aug. 5, 2008, recorded: May 2008, views: 5388
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In this work we propose a sparse approximation for the full covariance matrix involved in the multiple output convolution process. We exploit the fact that each of the outputs is conditional independent of all others given the input process. This leads to an approximation for the covariance matrix which keeps intact the covariances of each output and approximates the cross-covariances terms with a low rank matrix. It has a similar form to the Partially Independent Training Conditional (PITC) approximation for a single output GP.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !