报告时间：2021/12/10 14:30-16:30

报告形式：腾讯会议：546-954-695

主持人：史建红教授

报告题目(Title)：Communication-Efficient Distributed Linear Discriminant Analysis for Binary Classification

报告摘要（Abstract）：Large-scale data are common when the sample size n is large, and these data are often stored on k different local machines. Distributed statisticallearning is an efficient way to deal with such data. In this study, we consider the binary classification problem for massive data based on a linear discriminant analysis (LDA) in a distributed learning framework. The classical centralized LDA requires the transmission of some p-by-p summary matrices to the hub, where p is the dimension of the variates under consideration. This can be a burden when p is large or the communication costs between the nodes are expensive. We consider two distributed LDA estimators, two-round and one-shot estimators, which are communication-efficient without transmitting p-by-p matrices. We study the asymptotic relative efficiency of distributed LDA estimators compared to a centralized LDA using random matrix theory under different settings of k.It is shown that when k is in a suitable range, such as k = o(n/p), these two distributed estimators achieve the same efficiency as that of the centralized estimator under mild conditions. Moreover, the two-round estimator can relax the restriction on k, allowing kp/n ->c 2 [0, 1) under some conditions. Simulations confirm the theoretical results.

报告人简介（Biography）：赵俊龙，北京师范大学统计学院教授，博士生导师，应用统计系主任。中国现场统计学会高维数据分会理事，北京应用统计学会理事，北京大数据学会常务理事。主要研究领域包括高维数据分析、稳健统计，统计机器学习等。在统计学各类期刊发表SCI论文四十余篇，部分结果发表在统计学顶级期刊Journal of the Royal Statistical Society: Series B（JRSSB）、The Annals of Statistics（AOS）、Journal of American Statistical Association(JASA)，Biometrika等。主持多项国家自然科学基金面上项目，自然科学基金青年基金项目，教育部人文社科基金等科研项目。2013年获得北京航空航天大学“蓝天科研新星”。