A key issue in Gaussian Process modeling is to decide on the locations where measurements are going to be taken. A good set of observations will provide a better model. Current state of the art selects such a set so as to minimize the posterior variance of the Gaussian process by exploiting submodularity. We propose two techniques, a Gradient Descent procedure and an heuristic algorithm to iteratively improve an initial set of observations so as to minimize the posterior variance directly. Results show the clear improvements that can be obtain under different settings.

Original languageEnglish
Pages (from-to)727-734
Number of pages8
JournalPattern Recognition Letters
Volume125
DOIs
Publication statusPublished - 2019

    Research areas

  • Gaussian process, Gradient descent, Sampling, Variance reduction

ID: 56765018