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数学学院、所系列学术报告(745场):香港科技大学理学院院长 汪扬教授 学术报告

发表于: 2017-12-26 09:02  点击:

题 目:Data Recovery on Manifolds: A Theoretical Framework
报告人:香港科技大学理学院院长 汪扬教授
  间:2017年12月26日(星期二) 下午2:00
  点:数学学院第一报告厅

Abstract: Recovering data from compressed number of measurements is ubiquitous in applications today. Among the best know examples are compressed sensing and low rank matrix recovery. To some extend phase retrieval is another example. The general setup is that we would like to recover a data point lying on some manifold having a much lower dimension than the ambient dimension, and we are given a set of linear measurements. The number of measurements is typically much smaller than the ambient dimension.  So the questions become: Under what conditions can we recover the data point from these linear measurements? If so, how? The problem has links to classic algebraic geometry as well as some classical problems on the embedding of projective spaces into Euclidean spaces and nonsingular bilinear forms. In this talk I'll give a brief overview and discuss some of the recent progresses.