Sparse Variational Contaminated Noise Gaussian Process Regression for Forecasting Geomagnetic Perturbations

arXiv:2402.17570v1 Announce Type: new
Abstract: Gaussian Processes (GP) have become popular machine learning methods for kernel based learning on datasets with complicated covariance structures. In this paper, we present a novel extension to the GP framework using a contaminated normal likelihood function to better account for heteroscedastic variance and outlier noise. We propose a scalable inference algorithm based on the Sparse Variational Gaussian Process (SVGP) method for fitting sparse Gaussian process regression models with contaminated normal noise on large datasets. …

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