We propose a new method for simplification of Gaussian process (GP) models by
projecting the information contained in the full encompassing model and
selecting a reduced number of variables based on their predictive relevance.
Our results on synthetic and real world datasets show that the proposed method
improves the assessment of variable relevance compared to the automatic
relevance determination (ARD) via the length-scale parameters. We expect the
method to be useful for improving explainability of the models, reducing the
future measurement costs and reducing the computation time for making new
predictions.Comment: A few minor changes in tex