题 目： Regularized Incremental Linear Discriminant Analysis on Large-Scale Data
报告人：储德林教授，新加坡国立大学
时 间：2018年6月22日 9:30-10:30
地 点：3-309
Abstract: Over the past few decades, a lot of attention has been drawn to large-scale streaming data analysis, where researchers are faced with huge amount of high-dimensional data acquired in a stream fashion. In this case, conventional algorithms that compute the result from scratch whenever a new data comes are highly inefficient. To handle this problem, we propose a new incremental regularized least squares algorithm that is applied to supervised dimensionality reduction of large-scale streaming data with focus on linear discriminant analysis. Experimental results on real-world data sets demonstrate the effectiveness and efficiency of our algorithms.
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、University of Bielefeld等高校工作过。主要研究领域是科学计算、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。任Automatica期刊的副主编，Journal of Computational and Applied Mathematics的顾问编委，Journal of The Franklin Institute期刊的客座编委。
