时 间：2021年12月17日（周五）10：00-11：00
地 点：理科大楼A302室
题 目：A Portmanteau Local Feature Discrimination Approach to the Classification with High-dimensional Matrix-variate Data
主讲人：罗珊 上海交通大学副教授
主持人：王小舟 助理教授
摘 要：
Matrix-variate data arise in many scientific fields such as face recognition, medical imaging, etc. Matrix data contain important structure information which can be ruined by vectorization. Methods incorporating the structure information into analysis have significant advantages over vectorization approaches. In this article, we consider the problem of two-class classification with high-dimensional matrix-variate data, and propose a novel portmanteaulocal-feature discrimination (PLFD) method. This method first identifies local discrimination features of the matrix variate and then pools them together to construct a discrimination rule. We investigated the theoretical properties of the PLFD method and established its asymptotic optimality. We carried out extensive numerical studies including simulation and real data analysis to compare this method with other methods available in the literature, which demonstrate that the PLFD method has a great advantage over the other methods in terms of misclassification rate.
报告人简介：
罗珊，新加坡国立大学统计学博士，密歇根大学生物统计系访问学者。现为上海交通大学数学科学学院长聘副教授。主要研究领域为高维向量和矩阵数据、函数型数据、时空数据中的分类问题、模型选择标准和变量选择方法。文章主要发表在Journal of the American Statistical Association，Statistica Sinica，Journal of Multivariate Analysis，Sankhya A, Annals of the Institute of Statistical Mathematics，Computational Statistics and Data Analysis, Journal of Statistical Planning and Inference等期刊上。