Journal of Data Mining in Genomics & Proteomics

Innovated interaction screening for high-dimensional nonlinear classification

2^nd International Conference on Big Data Analysis and Data Mining

November 30-December 01, 2015 San Antonio, USA

Daoji Li

University of Central Florida, USA

Posters-Accepted Abstracts: J Data Mining In Genomics & Proteomics

Abstract:

This paper 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 twoway interactions of order O(p2). 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.

Biography :

Daoji Li recieved his PhD from the University of Manchester. Before joining the University of Central Florida, he was a Post-doctoral Research Associate at University of Southern California Marshall School of Business. His research interests include big data problems, high dimensional statistical inference, variable selection and machine learning, classification, longitudinal data analysis and survival analysis. His papers have been published in the leading statistics journals, including Annals of Statistics.

Email: Daoji.Li@ucf.edu