  We discuss the correspondence between Gaussian process regression and
Geometric Harmonics, two similar kernel-based methods that are typically used
in different contexts. Research communities surrounding the two concepts often
pursue different goals. Results from both camps can be successfully combined,
providing alternative interpretations of uncertainty in terms of error
estimation, or leading towards accelerated Bayesian Optimization due to
dimensionality reduction.
