主 题：Ultrahigh dimensional multi-class linear discriminant analysis by pairwise sure independence screening
主讲人：潘蕊博士
主持人：兰伟博士
时 间：2015年5月12日上午10:30-11:30
地 点：通博楼B212学术会议室
主办单位：统计研究中心 统计学院 科研处
主讲人简介：
潘蕊博士，毕业于北京大学光华管理学院，商务统计与经济统计系，师从王汉生教授，2012年访问美国加州大学戴维斯分校商学院蔡知令教授。现就职于中央财经大学统计于数学学院。主要研究兴趣是：超高维数据变量选择、网络结构数据的统计分析等
内容提要：
This paper is concerned with the problem of feature screening for multi-class linear discriminant analysis under ultrahigh dimensional setting. We allow the number of classes to be relatively large. As a result, the total number of relevant features is larger than usual. This makes the related classification problem much more challenging than the conventional one, where the number of classes is small (very often two). To solve the problem, we propose a novel pairwise sure independence screening method for linear discriminant analysis with an ultrahigh dimensional predictor. The proposed procedure is directly applicable to the situation with many classes. We further prove that the proposed method is screening consistent. Simulation studies are conducted to assess the finite sample performance of the new procedure. We also demonstrate the proposed methodology via an empirical analysis of a real life example on handwritten Chinese character recognition.
Keywords: Multi-class Linear Discriminant Analysis; Pairwise Sure Independence
Screening; Sure Independence Screening; Strong Screening Consistency
