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

• URL: http://arxiv.org/abs/2411.07800v1
• Date: Tue, 12 Nov 2024 13:54:13 GMT
• Title: Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows
• Authors: Zina-Sabrina Duma, Tuomas Sihvonen, Jouni Susiluoto, Otto Lamminpää, Heikki Haario, Satu-Pia Reinikainen,
• Abstract summary: Kernel-based statistical methods are efficient, but their performance depends heavily on the selection of kernel parameters. 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.
• Score: 0.0
• License:
• Abstract: 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.

Related papers

• Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach [0.19999259391104385]
  This paper examines the application of the Kernel Sum of Squares (KSOS) method for enhancing kernel learning from data. Traditional kernel-based methods frequently struggle with selecting optimal base kernels and parameter tuning. KSOS mitigates these issues by leveraging a global optimization framework with kernel-based surrogate functions.
  arXiv  Detail & Related papers  (2024-08-12T19:32:28Z)
• Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning [33.34053480377887]
  This paper enhances kernel ridgeless regression with Locally-Adaptive-Bandwidths (LAB) RBF kernels. For the first time, we demonstrate that functions learned from LAB RBF kernels belong to an integral space of Reproducible Kernel Hilbert Spaces (RKHSs)
  arXiv  Detail & Related papers  (2024-06-03T15:28:12Z)
• KF-PLS: Optimizing Kernel Partial Least-Squares (K-PLS) with Kernel Flows [0.0]
  Kernel PLS (K-PLS) has been introduced for modelling non-linear predictor-response relations. In K-PLS, the input data is mapped via a kernel function to a Reproducing Kernel Hilbert space (RKH), where the dependencies between the response and the input matrix are assumed to be linear. We propose a methodology for the kernel function optimization based on Kernel Flows (KF), a technique developed for Gaussian process regression (GPR)
  arXiv  Detail & Related papers  (2023-12-11T17:32:36Z)
• On the Sublinear Regret of GP-UCB [58.25014663727544]
  We show that the Gaussian Process Upper Confidence Bound (GP-UCB) algorithm enjoys nearly optimal regret rates. Our improvements rely on a key technical contribution -- regularizing kernel ridge estimators in proportion to the smoothness of the underlying kernel.
  arXiv  Detail & Related papers  (2023-07-14T13:56:11Z)
• Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics [0.4588028371034406]
  We introduce algorithms to select/design kernels in Gaussian process regression/kriging surrogate modeling techniques. A first class of algorithms is kernel flow, which was introduced in a context of classification in machine learning. A second class of algorithms is called spectral kernel ridge regression, and aims at selecting a "best" kernel such that the norm of the function to be approximated is minimal.
  arXiv  Detail & Related papers  (2022-06-03T07:50:54Z)
• Kernel Identification Through Transformers [54.3795894579111]
  Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models. This work addresses the challenge of constructing custom kernel functions for high-dimensional GP regression models. We introduce a novel approach named KITT: Kernel Identification Through Transformers.
  arXiv  Detail & Related papers  (2021-06-15T14:32:38Z)
• Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition [54.07797071198249]
  We introduce a new scalable variational Gaussian process approximation which provides a high fidelity approximation while retaining general applicability. We demonstrate that, on a range of regression and classification problems, our approach can exploit input space symmetries such as translations and reflections. Notably, our approach achieves state-of-the-art results on CIFAR-10 among pure GP models.
  arXiv  Detail & Related papers  (2021-06-10T18:17:57Z)
• Flow-based Kernel Prior with Application to Blind Super-Resolution [143.21527713002354]
  Kernel estimation is generally one of the key problems for blind image super-resolution (SR) This paper proposes a normalizing flow-based kernel prior (FKP) for kernel modeling. Experiments on synthetic and real-world images demonstrate that the proposed FKP can significantly improve the kernel estimation accuracy.
  arXiv  Detail & Related papers  (2021-03-29T22:37:06Z)
• SimpleMKKM: Simple Multiple Kernel K-means [49.500663154085586]
  We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM) Our criterion is given by an intractable minimization-maximization problem in the kernel coefficient and clustering partition matrix. We theoretically analyze the performance of SimpleMKKM in terms of its clustering generalization error.
  arXiv  Detail & Related papers  (2020-05-11T10:06:40Z)
• Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystr\"om method [76.73096213472897]
  We develop techniques which exploit spectral properties of the data matrix to obtain improved approximation guarantees. Our approach leads to significantly better bounds for datasets with known rates of singular value decay. We show that both our improved bounds and the multiple-descent curve can be observed on real datasets simply by varying the RBF parameter.
  arXiv  Detail & Related papers  (2020-02-21T00:43:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.