The Annals of Statistics

Innovated interaction screening for high-dimensional nonlinear classification

Yingying Fan, Yinfei Kong, Daoji Li, and Zemin Zheng

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This paper is concerned with the problems of interaction screening and nonlinear classification in a 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.

Article information

Ann. Statist., Volume 43, Number 3 (2015), 1243-1272.

Received: October 2014
First available in Project Euclid: 15 May 2015

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62H30: Classification and discrimination; cluster analysis [See also 68T10, 91C20]
Secondary: 62F05: Asymptotic properties of tests 62J12: Generalized linear models

Classification dimension reduction discriminant analysis interaction screening sparsity sure screening property


Fan, Yingying; Kong, Yinfei; Li, Daoji; Zheng, Zemin. Innovated interaction screening for high-dimensional nonlinear classification. Ann. Statist. 43 (2015), no. 3, 1243--1272. doi:10.1214/14-AOS1308.

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