用L1正则化支持向量机实现多变量函数型数据的分类与特征选择
L1-Regularized Functional Support Vector Machine
- 引入L1正则化，从多个函数型变量中筛选出相关特征
- 在模拟和真实数据上均表现出优异的预测准确率和变量选择能力
- 适合处理高维函数型数据的二分类问题，如生物信号分析
在函数型数据分析中，仅含一个函数型协变量的二分类问题已得到广泛研究。本文旨在填补多变量函数型协变量分类研究的空白，提出一种针对二分类任务的L1正则化函数型支持向量机。配套算法被开发用于拟合该分类器。通过施加L1惩罚项，该方法能够识别与二分类响应相关的函数型协变量。模拟实验和一个真实数据应用的结果表明，所提出的分类器在预测性能和特征选择方面均表现良好。
原文摘要 · Abstract (English)
In functional data analysis, binary classification with one functional covariate has been extensively studied. We aim to fill in the gap of considering multivariate functional covariates in classification. In particular, we propose an $L_1$-regularized functional support vector machine for binary classification. An accompanying algorithm is developed to fit the classifier. By imposing an $L_1$ penalty, the algorithm enables us to identify relevant functional covariates of the binary response. Numerical results from simulations and one real-world application demonstrate that the proposed classifier enjoys good performance in both prediction and feature selection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。