学术报告
Recovery of High-Dimensional Low-Rank Matrices
题 目： Recovery of High-Dimensional Low-Rank Matrices
报告人：Professor Tony Cai（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.
时间：5月3日（周二）下午4:00-5:00
地点：首都师大北一区文科楼506教室
欢迎全体师生积极参加！
