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Innovated Interaction Screening for High-Dimensional Classification
Date: 2016/6/22             Browse: 596

Innovated Interaction Screening for High-Dimensional Classification

Speaker: Daoji Li

Time: Jun 22, 10:30am – 11:30am.

Location: Room 220, No. 8 Building, Yueyang Road


This talk is concerned with the problems of interaction screening and nonlinear classification in high-dimensional setting.  We propose a two-step procedure, IIS-SQDA, where in the first step an innovated interaction screening (IIS) approach based on transforming the original p-dimensional feature vector is proposed, and in the second step a sparse quadratic discriminant analysis (SQDA) is proposed for further selecting important interactions and main effects and simultaneously conducting classification. Our IIS approach screens important interactions by examining only p features instead of all two-way interactions of order O(p^2). Our theory shows that the proposed method enjoys sure screening property in interaction selection in the high-dimensional setting of p growing exponentially with the sample size. In the selection and classification step, we establish a sparse inequality on the estimated coefficient vector for QDA and prove that the classification error of our procedure can be upper-bounded by the oracle classification error plus some smaller order term. Extensive simulation studies and real data analysis show that our proposal compares favorably with existing methods in interaction selection and high dimensional classification.


Dr. Daoji Li is currently an Assistant Professor in the Department of Statistics at University of Central Florida, United States.  From 2012 to 2015, he was a Postdoctoral Associate Scholar at Marshall School of Business, University of Southern California.  He received his Ph.D. in Statistics from University of Manchester, United Kingdom.  His research interests include high-dimensional statistics, big data analytics, machine learning and data mining. 

His papers have been published in top tier journals, including the Annals of Statistics.  In 2006 He received Overseas Research Studentship (ORS) award from UK's Secretary of State for Education and Science.  He is a member of the American Statistical Association, the International Statistical Institute and the International Chinese Statistical Association. 

SIST-Seminar 16046