L1-norm quantile regression screening rule via the dual circumscribed sphere
报告人：孔令臣
报告人单位：北京交通大学
时间：22年6月16日（周四）晚上 8：30~9：30
线上腾讯会议号：207-468-447 密码：610064
摘要：L1-norm quantile regression is a common choice if there exists outlier or heavy-tailed error in high-dimensional data sets. However, it is computationally expensive to solve this problem when the feature size of data is ultra-high. As far as we know, existing screening rules cannot speed up the computation of the l1-norm quantile regression, which dues to the non-differentiability of the quantile function/pinball loss. In this paper, we introduce the dual circumscribed sphere technique and propose a novel l1-norm quantile regression screening rule. Our rule is expressed as the closed-form function of given data and eliminates inactive features with a low computational cost. Numerical experiments on some simulation and real data sets show that this screening rule can be used to eliminate almost all inactive features. Moreover, this rule can help to reduce up to 23 times of computational time, compared with the computation without our screening rule.
报告人简介：孔令臣，北京交通大学理学院教授，博士生导师，中国运筹学会数学规划分会副秘书长。2007年毕业于北京交通大学，获博士学位。2007-2009年，加拿大滑铁卢大学组合与优化系博士后。2009年9月入职北京交通大学数学系，2010年晋升为副教授，2014年晋升为教授。主要从事优化与统计学习、高维统计分析、稀疏优化、对称锥互补和优化问题以及医学成像等方面的研究。主持国家自然科学基金面上项目和参与973课题、国家自然科学基金重点项目以及北京市自然科学基金重点项目等，获得2012度中国运筹学会青年奖。
