Title：Model-free Variable Selection via Learning Gradients
Speaker:Prof.Junhui Wang,Department of Mathematics,City University of Hong Kong
Date:2015.5.27(Wednesday)1:30-2:30 PM
Place:苏州大学本部维格堂113
Abstract: In recent years, variable selection has attracted enormous attention from statistics community. A wide spectrum of variable selection algorithms have been proposed based on various model assumption. In this talk, we will propose a general model-free variable selection framework. As opposed to existing algorithms, the key advantage of the proposed framework is that it assumes no distributional model, admits general predictor effects, allows for efficient computation, and attains desirable theoretical properties. The proposed framework is formulated in the form of gradient learning in a re- producing kernel Hilbert space, which enjoys the power of and extended representor theorem and thus enables efficient learning of sparse gradients. The proposed framework is implemented via a scalable block coordinate de- scent algorithm. The advantage is demonstrated in a variety of simulated experiments as well as real datasets. If time permits, the asymptotic con- sistencies will be discussed.
报告人简介:王军辉博士，香港城市大学数学系副教授，曾任芝加哥伊利诺伊大学副教授，主要研究：统计机器学习和数据挖掘，模式的评估和选择，高维数据分析以及在工程、金融学和生物医学科学中的应用。
详见个人网页://www6.cityu.edu.hk/ma/people/profile/wangjh.htm
Model-free Variable Selection via Learning Gradients