学术报告：Regularized Modal Regression with Applications in Cognitive Impairment Prediction

报告摘要：Linear regression models have been successfully used to function estimation and model selection in high-dimensional data analysis. However, most existing methods are built on least squares with the mean square error (MSE) criterion. In this talk, we go beyond this criterion by investigating the regularized modal regression from a statistical learning viewpoint. A new regularized modal regression model is proposed for estimation and variable selection, which is robust to outliers, heavy-tailed noise, and skewed noise. On the theoretical side, we establish the approximation estimate for learning the conditional mode function, the sparsity analysis for variable selection, and the robustness characterization. On the application side, we applied our model to improve the cognitive impairment prediction using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort data.

主办单位：英国立博官网中文版

报告专家：陈洪

报告时间：2018年5月28日（星期一）上午10：00

报告地点：201报告厅

专家简介：陈洪，华中农业大学教授，博士生导师，湖北省优秀博士论文获得者。研究方向为机器学习理论、逼近论。2009.6博士毕业于湖北大学基础数学专业。主持国家自然科学基金面上项目、国家自然科学基金青年基金项目、校优秀人才培育项目、校创新团队培育项目等多项科研课。发表SCI论文30余篇，包括Applied and Computational Harmonic Analysis,IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, Neural Computation, Neural Networks, Journal of Approximation Theory等知名学术期刊，在机器学习顶级会议NIPS发表论文3篇。2016.3-2017.8受美方资助作为博后研究员在University of Texas at Arlington从事合作研究，多次受邀请和资助赴澳门大学、香港城市大学等进行合作研究。