• 演讲人： Chen Xin
• 时间：2019年04月25日 周四下午2:00
• 地点：紫金港校区行政楼14层1417报告厅
• 主办单位：浙江大学数据科学研究中心

In this talk, we talk about a new sufficient dimension reduction approach designed deliberately for high-dimensional classification. This novel method is named maximal mean variance (MMV), inspired by the mean variance index first proposed by Cui, Li and Zhong (2015), which measures the dependence between a categorical random variable with multiple classes and a continuous random variable. Our method requires reasonably mild restrictions on the predicting variables and keeps the model-free advantage without the need to estimate the link function. Our method works pretty well when n < p. The surprising classification efficiency gain of the proposed method is demonstrated by simulation studies and real data analysis.

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联系人: 骆威老师 weiluo@zju.edu.cn

            浙江大学数据科学研究中心

报告人简介：

Dr. Chen got his bachelor degree from Nankai University and his PHD from University of Minnesota. He currently works in Southern University of Science and Technology. His research area includes dimension reduction, variable selection and high dimensional analysis.