Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows

Autor:	Duma, Zina-Sabrina, Sihvonen, Tuomas, Susiluoto, Jouni, Lamminpää, Otto, Haario, Heikki, Reinikainen, Satu-Pia
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Computational Engineering Finance and Science Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.07800 Zobrazit plný text záznamu View this record from Arxiv