报告时间：5月10日（周二）16:00-17:00
报告地点：统计楼105报告厅
报告题目：Recovery of High-Dimensional Low-Rank Matrices
报告人：Tony Cai，Department of Statistics，The Wharton School，University of Pennsylvania
摘要：
Low-rank structure commonly arises in many applications including genomics, signal processing, and portfolio allocation. It is also used in many statistical inference methodologies such as principal component analysis. In this talk, I will present some recent results on recovery of a high-dimensional low-rank matrix with rank-one measurements and related problems including phase retrieval and optimal estimation of a spiked covariance matrix based on one-dimensional projections. I will also discuss structured matrix completion which aims to recover a low rank matrix based on incomplete, but structured observations.