【专家简介】：徐晨，鹏城国家实验室研究员、西安交通大学领军学者、国家级海外高层次人才计划入选者、深圳孔雀计划特聘专家。2012年于加拿大不列颠哥伦比亚大学取得统计学博士学位，先后赴美国宾州州立大学、加拿大渥太华大学工作任教。徐晨教授长期从事大数据统计机器学习的基础理论与方法研究，在大数据特征筛选/降维、再抽样理论与方法、分布式统计分析等领域取得系统性创新成果，做出多个原创性贡献。在统计学与机器学习国际著名杂志及会议发表研究论文40余篇; 主持中加多项国家级科研项目。现任统计学权威杂志JASA、EJS的副主编，曾任CJS、Neurocomputing、Survey Sampling等国际知名杂志的编委或客座主编。
【报告摘要】：Finite mixture of regression models are ubiquitous for analyzing complex data. They aim to detect heterogeneity in the effects of a set of features on a response over a finite number of latent classes. When the number of features is large, a direct fitting of mixture regressions can be computationally infeasible and often leads to a poor interpretative value. One practical strategy is to screen out most irrelevant features before an in-depth analysis. In this paper, we propose a novel method for feature screening in ultrahigh-dimensional Gaussian finite mixture of regressions. The new method is built upon a sparsity-restricted expectation-approximation-maximization algorithm, which simultaneously removes varying sets of irrelevant features from multiple latent classes. In the screening process, joint effects between features are naturally accounted and class-specific screening results are produced without ad hoc steps. These merits give the new method an edge to outperform the existing screening methods. The promising performance of the method is supported by both theory and numerical examples including a real data analysis.
时间：2024年04月12日 15:30 – 16:30
会议地点：位育楼407