English(EN) Semi-parametric Functional Classification via Path Signatures Logistic Regression with Adaptive Order Selection

新的PSLR框架通过路径签名增强函数数据分类

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-07 04:00

研究人员推出了一种新颖的半参数框架——路径签名逻辑回归（PSLR），用于对向量值函数数据进行分类。该方法利用路径签名来表示数据，对不规则采样具有鲁棒性，并能捕捉跨通道依赖性。一项关键创新是其半参数加性结构，它为标量协变量保留了可解释的线性效应，并采用数据驱动的方法来自适应地选择签名截断阶数。实验表明，PSLR在准确性和可解释性方面优于传统的函数分类器和固定阶数签名基线。 AI

影响这一新的分类框架有望提高各种数据分析任务中机器学习模型的准确性和可解释性。

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arXiv stat.ML TIER_1 English(EN) · Pengcheng Zeng, Siyuan Jiang · 2026-07-07 04:00

基于路径签名逻辑回归和自适应阶数选择的半参数函数分类

arXiv:2507.06637v2 Announce Type: replace Abstract: We propose Path Signatures Logistic Regression (PSLR), a semi-parametric framework for classifying vector-valued functional data with scalar covariates. Classical functional logistic regression models rely on linear assumptions …

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基于路径签名逻辑回归和自适应阶数选择的半参数函数分类

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