Matrix Completion under Low-Rank Missing Mechanism

发布者:张建涛发布时间:2019-05-23浏览次数:262

统计与数据科学学院


---------------------------------------------

                   学术讲座


讲座题目:Matrix Completion under Low-Rank Missing Mechanism


讲座人:毛晓军(复旦大学)     


时    间:2019年6月3日(周一)上午8:00-9:00   


地    点:统计与数据科学学院126教室


摘    要:Matrix completion is a modern missing data problem where both the missing structure and the underlying parameter are high dimensional. Although missing structure is a key component to any missing data problems, existing matrix completion methods often assume a simple uniform missing mechanism. In this work, we study matrix completion from corrupted data under a novel low-rank missing mechanism. The probability matrix of observation is estimated via a high dimensional low-rank matrix estimation procedure, and further used to complete the target matrix via inverse probabilities weighting. Due to both high dimensional and extreme (i.e., very small) nature of the true probability matrix, the effect of inverse probability weighting requires careful study. We derive optimal asymptotic convergence rates of the proposed estimators for both the observation probabilities and the target matrix.


邀  请  人:王磊博士

 


欢迎广大师生参加! 

统计与数据科学学院


2019年5月23日