arXiv：cs.LG· Andrea Angino, Matthias Voigt, Rolf Krause, Tam\'as D\'ozsa·· 4 小时前AI 评分32
变量投影 SVM 模型的二阶优化与道路异常检测
Second-order optimization of variable projection SVM models and road abnormality detection
AI 导读
研究提出一种用于最小化变量投影泛函的二阶优化框架，可高效训练基于变量投影的核方法，尤其是变量投影支持向量机（VP-SVM）。该框架采用二阶信赖域算法训练 VP-SVM，在真实应用中基于轮胎传感器采集的 1D 信号识别路面异常。相关成果发表于 ICASSP 2026。
正文
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.
| Subjects: | Signal Processing (eess.SP); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.09617 [eess.SP] |
| (or arXiv:2610.09617v1 [eess.SP] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09617 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2026), Barcelona, Spain, 2026 |
| Related DOI: | https://doi.org/10.1109/ICASSP55912.2026.11463454
DOI(s) linking to related resources |
Submission history
From: Andrea Angino [view email]
[v1]
Wed, 7 Oct 2026 07:57:51 UTC (40 KB)
来源：arXiv：cs.LG · arxiv.org