发表机构
UniDistance Suisse; KAUST（瑞士远程学习大学; 阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种二阶优化框架用于训练变分投影支持向量机，并在轮胎传感器1D信号的路面异常检测应用中验证其有效性。
AI 中文摘要
我们提出了一种新颖的二阶优化框架，用于最小化所谓的变分投影泛函。我们证明该框架对于基于变分投影的核方法的训练尤为有用。特别地，我们考虑了高效训练变分投影支持向量机（VP-SVMs）的问题。我们在一个实际应用中展示了所提出训练方法的有效性，即我们演示了如何使用二阶信赖域算法训练VPSVM模型，以基于从轮胎传感器获得的1D信号来识别路面异常。
英文摘要
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.
Journal refProc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2026), Barcelona, Spain, 2026
DOI：10.1109/ICASSP55912.2026.11463454
