Kernel-based statistical methods are efficient, but their performance depends heavily on the selection of kernel parameters. In literature, the optimization studies on kernel-based chemometric methods is limited and often reduced to grid searching. Previously, the authors introduced Kernel Flows (KF) to learn kernel parameters for Kernel Partial Least-Squares (K-PLS) regression. KF is easy to implement and helps minimize overfitting. In cases of high collinearity between spectra and biogeophysical quantities in spectroscopy, simpler methods like Principal Component Regression (PCR) may be more suitable. In this study, we propose a new KF-type approach to optimize Kernel Principal Component Regression (K-PCR) and test it alongside KF-PLS. Both methods are benchmarked against non-linear regression techniques using two hyperspectral remote sensing datasets.

翻译：核统计方法具有高效性，但其性能在很大程度上取决于核参数的选择。现有文献中，针对基于核的化学计量学方法的优化研究较为有限，且通常简化为网格搜索。此前，作者引入核流（KF）方法以学习核偏最小二乘（K-PLS）回归的核参数。KF易于实现，并有助于最小化过拟合。在光谱学中光谱与生物地球物理量存在高共线性的情况下，主成分回归（PCR）等更简单的方法可能更为适用。本研究提出一种新型KF类方法以优化核主成分回归（K-PCR），并与KF-PLS进行同步测试。利用两个高光谱遥感数据集，将两种方法与非线性回归技术进行了基准对比。