Given a reproducing kernel Hilbert space H of real-valued functions and a
suitable measure mu over the source space D (subset of R), we decompose H as
the sum of a subspace of centered functions for mu and its orthogonal in H.
This decomposition leads to a special case of ANOVA kernels, for which the
functional ANOVA representation of the best predictor can be elegantly derived,
either in an interpolation or regularization framework. The proposed kernels
appear to be particularly convenient for analyzing the e ffect of each (group
of) variable(s) and computing sensitivity indices without recursivity.

本文针对实值函数的再生核希尔伯特空间 H 以及源空间 D（R 的子集）中适当的度量 mu，将 H 分解为 mu 的中心函数和其在 H 中正交的子空间之和。这种分解导致了 ANOVA 核的一个特殊情况，对于该情况可以优雅地在插值或正则化框架中推导出最佳预测器的函数 ANOVA 表示。提出的核似乎特别方便，用于分析每个变量或变量组的影响并计算灵敏度指标，无需递归。