5月10日 Tony Cai:Recovery of High-Dimensional Low-Rank Matrices

时间:2016-05-09浏览:349设置

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

告题目Recovery of High-Dimensional Low-Rank Matrices

报告人Tony Cai,Department of Statistics,The Wharton School,University of Pennsylvania

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.


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