讲座题目：Distance Shrinkage and Euclidean Embedding via Regularized Kernel Estimation
主讲人：袁明教授 美国威斯康辛麦迪逊大学统计系
讲座时间：2016年12月26日（周一） 16:00
讲座地点：数学学院学术报告厅
欢迎各位老师和同学参加！
Abstract: Although recovering an Euclidean distance matrix from noisy observations is a common problem in practice, how well this could be done remains largely unknown. To fill in this void, we study a simple distance matrix estimate based upon the so-called regularized kernel estimate. We show that such an estimate can be characterized as simply applying a constant amount of shrinkage to all observed pairwise distances. This fact allows us to establish risk bounds for the estimate implying that the true distances can be estimated consistently in an average sense as the number of objects increases. In addition, such a characterization suggests an efficient algorithm to compute the distance matrix estimator, as an alternative to the usual second order cone programming known not to scale well for large problems.
主讲人简介：
袁教授2004年在美国威斯康辛麦迪逊大学统计系毕业获得博士学位。之后进入美国佐治亚理工工业工程系作为助理教授。2013年起在美国威斯康辛麦迪逊大学统计系做教授。同时也是Morgridge Institute for Research的Senior Investigator。2009年获得美国NSF的CAREER Award，2015年获得IMS的Fellow.