报告题目:CUR Decompositions and their Applications

报告人：黄龙秀

报告时间：2022年12月12日 09:30-10:30

报告地点：腾讯会议：934-865-455

摘要: In modern data analysis, the datasets are often represented by large-scale matrices or tensors (the generalization of matrices to higher dimensions). To have a better understanding or extract values effectively from  these data, an important step is to construct a low-dimensional / compressed representation of the data that may be better to analyze and interpret in light of a corpus of field-specific information. To implement the goal, a primary tool is the matrix / tensor decomposition. In this talk, I will talk about novel matrix/tensor decompositions, CUR decompositions, which are memory efficient and computationally cheap. Besides, I will also discuss the applications of CUR decompositions on developing efficient algorithms or models to data completion or robust decomposition problems. Additionally, some simulation results will be provided on real and synthetic datasets.

报告人简介: Longxiu Huang (黄龙秀) is currently Assistant Professor in the Department of Computational Mathematics, Science and Engineering and Department of Mathematics at Michigan State University. Dr. Huang received her PhD in Mathematics from Vanderbilt University with Prof. Akram Aldroubi and worked in UCLA before joining MSU in 2022. Her main research interests lie in Applied Harmonic Analysis, Numerical linear algebra, Data Analysis, Random matrix analysis, etc.

Homepage: http://longxiuhuang.com/

邀请人：张理评