科学研究
报告题目:

Kernel Variable Importance Measure with Applications

报告人:

黄丙耀 研究员(广东工业大学)

报告时间:

报告地点:

腾讯会议 ID:185 974 726

报告摘要:

This paper introduces a novel kernel variable importance measure (KvIM) based on the maximum mean discrepancy (MMD). KvIM can effectively measure the importance of each individual dimension in contributing to the distributional difference by constructing weighted MMD and applying perturbations to evaluate changes in MMD through assigned weights. KvIM has several notable advantages: it is nonparametric and model-free, accounts for dependencies among dimensions, and is suitable for high-dimensional data. Additionally, we establish the consistency of the empirical KvIM under general conditions, along with its theoretical properties in high-dimensional settings. Furthermore, we apply KvIM to classification problems and streaming datasets, proposing a KvIM-enhanced classification approach and the online KvIM. These applications demonstrate the practical utility of the proposed KvIM in diverse scenarios, justified via extensive numerical experiments.