Kinetic profile inference with outlier detection using support vector machine regression and Gaussian process regression

We propose an outlier-resilient Gaussian process regression (GPR) model supported by support vector machine regression (SVMR) for kinetic profile inference. GPR, being a non-parametric regression using Bayesian statistics, has advantages in that it imposes no constraints on profile shapes and can be readily used to integrate different kinds of diagnostics, while it is vulnerable to the presence of even a single outlier among a measured dataset. As an outlier classifier, an optimized SVMR is developed based only on the measurements. Hyper-parameters of the developed GPR model with informative prior distributions are treated in two different ways, i.e. maximum a posteriori (MAP) estimator and marginalization using a Markov Chain Monte Carlo sampler. Our SVMR-supported GPR model is applied to infer ion temperature Ti profiles using measured data from the KSTAR charge exchange spectroscopy system. The GPR-inferred Ti profiles with and without an outlier are compared and show prominent improvement when the outlier is removed by the SVMR. Ti profiles inferred with the MAP estimator and the marginalization scheme are compared. They are noticeably different when observation uncertainties are not small enough, and the marginalization scheme generally provides a smoother profile.