Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915015775223808 |
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| author | Duma, Zina-Sabrina Sihvonen, Tuomas Susiluoto, Jouni Lamminpää, Otto Haario, Heikki Reinikainen, Satu-Pia |
| author_facet | Duma, Zina-Sabrina Sihvonen, Tuomas Susiluoto, Jouni Lamminpää, Otto Haario, Heikki Reinikainen, Satu-Pia |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_07800 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows Duma, Zina-Sabrina Sihvonen, Tuomas Susiluoto, Jouni Lamminpää, Otto Haario, Heikki Reinikainen, Satu-Pia Machine Learning Computational Engineering, Finance, and Science Methodology 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. |
| title | Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows |
| topic | Machine Learning Computational Engineering, Finance, and Science Methodology |
| url | https://arxiv.org/abs/2411.07800 |