Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

2020

Journal or Book Title

Statistical Analysis and Data Mining

DOI

10.1002/sam.11459

Abstract

Sparse, knot‐based Gaussian processes have enjoyed considerable success as scalable approximations of full Gaussian processes. Certain sparse models can be derived through specific variational approximations to the true posterior, and knots can be selected to minimize the Kullback‐Leibler divergence between the approximate and true posterior. While this has been a successful approach, simultaneous optimization of knots can be slow due to the number of parameters being optimized. Furthermore, there have been few proposed methods for selecting the number of knots, and no experimental results exist in the literature. We propose using a one‐at‐a‐time knot selection algorithm based on Bayesian optimization to select the number and locations of knots. We showcase the competitive performance of this method relative to optimization of knots simultaneously on three benchmark datasets, but at a fraction of the computational cost.

Comments

This article is published as Garton, Nathaniel, Jarad Niemi, and Alicia Carriquiry. "Knot selection in sparse Gaussian processes with a variational objective function." Statistical Analysis and Data Mining: The ASA Data Science Journal (2020). doi: 10.1002/sam.11459.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

Share

COinS