报告人：赵俊龙
报告地点：腾讯会议ID:598-640-953
报告时间：2022年11月14日星期一14:30-15:30
报告摘要:
A problem of major interest in network data analysis is to explain the strength of connections using context information. To achieve this, we introduce a novel approach, called network supervised dimension reduction, in which covariates are projected onto low-dimensional spaces to reveal the linkage pattern without assuming a model. We propose a new loss function for estimating the parameters in the resulting linear projection, based on the notion that closer proximity in the low-dimension projection corresponds to stronger connections. Interestingly, the convergence rate of our estimator is found to depend on a network effect factor, which is the smallest number that can partition a graph in a manner similar to the graph colouring problem. Our method has interesting connections to principal component analysis and linear discriminant analysis, which we exploit for clustering and community detection. The proposed approach is further illustrated by numerical experiments and analysis of a pulsar candidates dataset from astronomy.
主讲人简介：
赵俊龙，北京师范大学统计学院教授。主要从事统计学和机器学习相关研究，包括：高维数据分析、统计机器学习、稳健统计等。 在统计学各类期刊发表SCI论文近五十篇，部分结果发表在统计学国际顶级期刊JRSSB，AOS、JASA，Biometrika等。主持多项国家自然科学基金项目，参与国家自然科学基金重点项目。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等。
