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数学科学学院学术报告
发布日期:2018-05-21 17:31:03 浏览次数:

报告题目:Feature Selection by the Principle of Correlation for High-dimensional Models with Complex Structures

报告摘要:The intrinsic mechanism of feature selection is correlation. For instance, in sequential approaches, the features are selected according to their Pearson's correlation with the residual of a current model, in penalised likelihood approaches, at a fixed value of the penalty parameter, the active set is indeed the set of features whose Pearson's correlations with the response exceed a certain threshold. In this talk, we discuss the principle of correlation search and consider its application to feature selection for high-dimensional models with complex structures. Specifically, we consider two such models: (i) a multi-response model where both the response variables and covariates have group structures, and (ii) an uni-response interaction model. For the first model, we develop a sequential canonical correlation search method. For the second model, we develop a sequential interaction group selection method. The asymptotic properties of these methods as well as simulation studies will be presented. These sequential methods can achieve selection consistency under meld conditions. The simulation studies demonstrate that they have an edge over other existing methods across a comprehensive simulation settings.

报 告 人:Zehua Chen ( National University of Singapore)

时    间:5月29日(星期二)下午3:00

地    点:瘦西湖校区38号楼108

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