统计与管理学院2015年学术报告第11期

【主  题】 Ultrahigh dimensional multi-class linear discriminant analysis by pairwise sure independence screening

【报告人】 潘蕊   博士

 中央财经大学

【时  间】 2015年4月9日（星期四）15:00－16:00

【地  点】 上海财经大学统计与管理学院大楼1312室

【语  言】 英文

【摘  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.