A Fast Frequent Directions Algorithm for Low Rank Approximation

报告题目：A Fast Frequent Directions Algorithm for Low Rank      

          Approximation

报告人：储德林教授 (新加坡国立大学)

报告时间：2020/11/12(周四)下午  15:40-16:40

报告形式：腾讯会议

会议ID：344 283 988

报告摘要：Recently, a deterministic method, frequent directions (FD) is proposed to solve the high dimensional low rank approximation problem. It works well in practice, but experiences high computational cost. In this talk we introduce a fast FD algorithm for the low rank approximation problem, which implants a randomized algorithm, sparse subspace embedding (SpEmb) in FD. This new algorithm makes use of FD's natural block structure and sends more information through SpEmb to each block in FD. It is proven that the new algorithm produces a good low rank approximation with a sketch of size linear on the rank approximated. Its effectiveness and efficiency are demonstrated by the experimental results on both synthetic and real world datasets.

报告人简介：储德林，新加坡国立大学教授。1982年考入清华大学，获学士、硕士、博士学位。先后在香港大学，清华大学，德国TU Chemnitz（开姆尼斯工业大学）、University of Bielefeld（比勒费尔德大学）等高校工作过。任SIAM Journal on Scientific Computing, SIAM Journal on Matrix Analysis and Applications, Automatica期刊的副主编，Journal of Computational and Applied Mathematics的顾问编委。主要研究领域是科学计算、数据科学、数值代数及其应用，在SIAM系列杂志，Numerische Mathematik，Mathematics of Computation，IEEE, Trans.，Automatica 等国际知名学术期刊发表论文一百余篇。