Multi-view data-driven ensemble kernel ridge regression via multi-kernel optimization

Kun Qu

doi:10.1007/s11042-026-21811-8

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Multi-view data-driven ensemble kernel ridge regression via multi-kernel optimization

2026-07-21

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Kun Qu

Zhifeng Liu

Emmanuel Ntaye

Ernest Domanaanmwi Ganaa

Xiang‐Jun Shen *

DOI：10.1007/s11042-026-21811-8

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Abstract

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Kernel Ridge Regression (E-KRR) is a widely used method for modeling nonlinear relationships, but it often suffers from manual kernel selection and sensitivity to parameter settings. These issues can lead to poor generalization and unstable performance across different datasets. In this paper, we propose an improved method called Ensemble Kernel Ridge Regression (E-KRR), which addresses these limitations by incorporating a multi-view learning approach. We begin by modeling data as having multiple views and extend traditional ridge regression to this setting. These views are then mapped into multiple kernel representations within different Reproducing Kernel Hilbert Spaces (RKHSs). E-KRR automatically learns optimal combinations of kernels and their weights directly from data, avoiding manual kernel tuning. Experimental results on 16 datasets demonstrate that E-KRR consistently outperforms several state-of-the-art methods. It achieves up to 11.4% lower MSE in regression tasks and improves classification accuracy by 2.8–4.3% on image datasets and 1.1–9.3% on tabular datasets, confirming its robustness and effectiveness.

Keywords:

Multi-view learning

Ensemble kernel ridge regression

Multi-kernel optimization

Nonlinear regression

Robust prediction

Reproducing kernel Hilbert space