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.

翻译：我们讨论高山进程回归和几何调和之间的对应关系,这两种相似的内核法通常在不同背景下使用。 围绕这两个概念的研究群体往往追求不同的目标。 两个营地的结果可以成功合并,对误差估计的不确定性提供替代解释,或者导致由于维度降低而加速巴耶斯最佳化。