Bagging for Gaussian process regression

Abstract

This paper proposes the application of bagging to obtain more robust and accurate predictions using Gaussian process regression models. The training data is re-sampled using the bootstrap method to form several training sets, from which multiple Gaussian process models are developed and combined through weighting to provide predictions. A number of weighting methods for model combination are discussed, including the simple averaging rule and the weighted averaging rules. We propose to weight the models by the inverse of their predictive variance, and thus the prediction uncertainty of the models is automatically accounted for. The bagging method for Gaussian process regression is successfully applied to the inferential estimation of quality variables in an industrial chemical plant.

Bayesian variable selection for Gaussian process regression: Application to chemometric calibration of spectrometers

“…Empirical comparative studies have confirmed the outstanding performance of Gaussian process regression with respect to other non-linear models [11,12,13]. As a result, Gaussian process models have been widely applied to various problems in statistics and engineering [14,11,15,16,17,18,13].…”

Approximate Inference in Related Multi-output Gaussian Process Regression

“…Traditionally, each subset of data learns a different model from another, this is done to increase the expressiveness in the model [11]. The final predictions are then made by combining the predictions of local experts [5].…”

Graph-based Semi-Supervised Regression and Its Extensions

“…GP has the computational problem due to an unfavorable cube scaling (O(N 3)) during training, where, N is the number of training data. In recent years, many methods have been proposed to address this problem: sparse GP approximation [24][20] [12], localized regression [7] [17]. In our work, we describe a clustering regression framework in order to bring the scaling down.…”