用核流方法优化高光谱图像的主成分回归，提升建模精度。
Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows
- 基于核流技术自动学习核参数，替代传统网格搜索。
- 在两个高光谱数据集上，新方法显著优于非线性回归基线。
- 适合光谱数据存在强共线性时的建模任务，如环境遥感。
基于核的统计方法虽高效，但性能高度依赖核参数选择。现有研究中针对核方法的优化多局限于网格搜索，缺乏系统性。此前作者提出核流（Kernel Flows, KF），用于学习核偏最小二乘（K-PLS）回归的核参数，实现易部署且抗过拟合。当光谱与生物地球物理量间存在高共线性时，主成分回归（PCR）等简化方法可能更合适。本文提出一种新型基于核流的核主成分回归（K-PCR）优化方法，并与KF-PLS进行对比。两种方法在两个高光谱遥感数据集上与非线性回归技术进行基准测试，验证了其有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。