Abstract Details
Activity Number:
|
309
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
Abstract #316516
|
View Presentation
|
Title:
|
Innovated Interaction Screening for High-Dimensional Nonlinear Classification
|
Author(s):
|
Yinfei Kong* and Yingying Fan and Daoji Li and Zemin Zheng
|
Companies:
|
and University of Southern California and University of Southern California and University of Southern California
|
Keywords:
|
Classification ;
dimension reduction ;
discriminant analysis ;
interaction screening ;
sparsity ;
sure screening property
|
Abstract:
|
This paper is concerned with 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. The IIS screens important interactions by examining only p features instead of all two-way interactions. 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 estimate of SQDA 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.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2015 program
|
For program information, contact the JSM Registration Department or phone (888) 231-3473.
For Professional Development information, contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
2015 JSM Online Program Home
ASA Meetings Department
732 North Washington Street, Alexandria, VA 22314
(703) 684-1221 • meetings@amstat.org
Copyright © American Statistical Association.