Rectangularization of Gaussian process regression for optimization of hyperparameters
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Optimization of hyperparameters of Gaussian process regression (GPR) determines success or failure of the application of the method. Such optimization is difficult with sparse data, in particular in high-dimensional spaces where the data sparsity issue cannot be resolved by adding more data. We show that parameter optimization is facilitated by rectangularization of the defining equation of GPR. On the example of a 15-dimensional molecular potential energy surface we demonstrate that this approach allows effective hyperparameter tuning even with very sparse data.
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