Second-order optimization of variable projection SVM models and road abnormality detection

Abstract:We introduce a novel second-order optimization framework for minimizing so-called variable projection functionals. We demonstrate that the proposed framework is especially usefulfor the training of variable projection based kernel methods. In particular, the problem of efficiently training variable projection support vector machines (VP-SVMs) is considered. We show the effectiveness of the proposed training methodology in a real-world application, namely we demonstrate how second-order trust region algorithms can be used to train VPSVM models to recognize road surface abnormalities based on 1D signals obtained from a tire sensor.

Signal Processing (eess.SP); Machine Learning (cs.LG)

arXiv:2610.09617 [eess.SP]

(or arXiv:2610.09617v1 [eess.SP] for this version)

https://doi.org/10.48550/arXiv.2610.09617 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2026), Barcelona, Spain, 2026

https://doi.org/10.1109/ICASSP55912.2026.11463454 Focus to learn more DOI(s) linking to related resources

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