Gaussian Processes for High-Dimensional, Large Data Sets: A Review
Mengrui Mina Jiang, Giulia Pedrielli, Szu Hui Ng · 2022
Gaussian processes, known to have versatile uses in several fields across engineering, science, economics, show important advantages to several alternative approaches while controlling model complexity. However, the use of this family of models is hindered for inputs that are high dimensional as well as large sample sizes due to the intractability of the likelihood function, and the growth of the variance covariance matrix. This article investigates state-of-art solutions to these challenges according classifying them into categories. The goal is to select several algorithms covering each category and perform empirical experiments to compare their performances on the same set of test functions. Our preliminary results focus on deterministic implementations of a set of selected approaches. The results of the experiments may serve as a guidance to future readers who want to study and use Gaussian process in problems with high dimensions and big data sets.