题目: Regularized Least Squares for Incremental Dimensionality Reduction of Large-Scale Data
报告人： 新加坡国立大学 储德林, 教授
时间： 7月14日 上午 9:00-10:00
地点： 精正楼 学术报告厅
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。于清华大学获得学士至博士学位。先后在清华大学, 香港科技大学，香港大学、德国TU Chemnitz、University of Bielefeld，比利时K.U.Leuven等高校工作过。主要研究领域是科学计算、数据挖掘， 数值代数及其应用，在SIAM系列杂志、Numerische Mathematik，Mathematics of Computation，Automatica, IEEE. Trans. Automatic Control, IEEE Trans. Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition 等国际顶级学术期刊发表论文一百余篇。任Automatica期刊副主编，Journal of Computational and Applied Mathematics顾问编委，Journal of the Franklin Institute期刊客座编委。