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Sufficient Dimension Reduction for Classification

编辑:wfy 时间:2019年04月18日 访问次数:636

题目:Sufficient Dimension Reduction for Classification

报告人:Xin Chen (南方科技大学)

时间:2019.04.25(周四)下午2:00

地点:紫金港校区管理学院行政楼14楼1417报告厅

摘要:

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.

欢迎广大师生踊跃参加!

联系人:骆威(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.