报告题目：Fast algorithms for large scale generalized distance weighted discrimination

报告人：孙德锋 教授 （新加坡国立大学）

时间：2016-05-14

地点：数学与统计学院一楼报告厅

摘要：High dimension low sample size statistical analysis is important in a wide range of applications. In such situations, the highly appealing discrimination method, support vector machine, can be improved to alleviate data piling at the margin. This leads naturally to the development of distance weighted discrimination (DWD), which can be modeled as a second-order cone programming problem and solved by interior-point methods when the scale (in sample size and feature dimension) of the data is moderate. Here, we design a scalable and robust algorithm for solving large scale generalized DWD problems. Numerical experiments on real data sets from the UCI repository demonstrate that our algorithm is highly efficient in solving large scale problems, and sometimes even more efficient than the highly optimized LIBSVM for solving the corresponding SVM problems. [This is a joint work with Xin Yee Lam, J. S. Marron and Kim-Chuan Toh]

报告时间：5月14日上午9:30

报告人简介：

孙德锋，新加坡国立大学数学系教授，现任国际顶级数学期刊Mathematical Programming和SIAM Journal on Optimization编委。曾任Asia-Pacific Journal of OperationalResearch主编。他于2011年5月，受邀在德国举行的SIAM优化国际会议上作大会报告。他的研究领域为最优化，上世纪90年代中后期在半光滑和光滑化牛顿方法方面取得国际公认的研究成果。本世纪初到现在，他开辟了矩阵优化这一新的学科，建立了非光滑矩阵分析，在矩阵优化的理论、算法及应用方面取得了奠基性的系列成果。