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

arXiv: https://arxiv.org/abs/2411.07800 · HTML: https://mlsanity.com/paper/2411.07800/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Kernel Flows: from learning kernels from data into the abyss](https://mlsanity.com/paper/1808.04475/) (2018)
- [KF-PLS: Optimizing Kernel Partial Least-Squares (K-PLS) with Kernel Flows](https://mlsanity.com/paper/2312.06547/) (2023)

## Abstract (arXiv)

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.