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来源: 日期:2021-12-09 作者: 浏览次数:

报告时间:2021/12/10 14:30-16:30



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”。