统计与管理学院2018年学术报告第24期

【主 题】 Reduced-Rank Linear Discriminant Analysis for Multi-class Classification

【报告人】 牛玥 副教授

University of Arizona

【时 间】 2018年06月15日（星期五）15:00-16:00

【地 点】 上海财经大学统计与管理学院大楼1208会议室

【摘 要】Many high dimensional classification techniques have been developed recently. However, most works focus on only the binary classification problem. Most classification tools for the multi-class cases are either based on over-simplified covariance structure or computationally complicated. In this talk, we introduce a new dimension reduction tool with the flavor of supervised principal component analysis. The proposed method is computationally efficient and can incorporate the correlation structure among the features. We illustrate our methods by simulated and real data examples. 

【嘉宾简介】Dr. Niu received her Ph.D. in operations research and financial engineering from Princeton University. She went to Yale University for her post-doctoral training. She has been working at University of Arizona since 2009. Her research interests include nonparametric statistics, high dimensional statistical learning and bioinformatics.