Abstract Details
Activity Number:
|
413
|
Type:
|
Topic Contributed
|
Date/Time:
|
Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
Abstract - #307988 |
Title:
|
Adaptively Weighted Large Margin Classifiers for Sufficient Dimension Reduction
|
Author(s):
|
Andreas Artemiou*+ and Yufeng Liu
|
Companies:
|
Michigan Technological University and The University of North Carolina
|
Keywords:
|
Sufficient Dimension Reduction ;
Inverse regression ;
Support Vector Machine ;
Adaptive weights ;
Robustness
|
Abstract:
|
Support Vector Machine(SVM) is a popular large-margin classifier. At the same time, sufficient dimension reduction is a powerful idea in dealing with high dimensional data. Recently Li, Artemiou and Li (2011) introduced Principal Support Vector Machine (PSVM), an algorithm which achieves linear and nonlinear dimension reduction under a unified framework by utilizing inverse regression and SVM ideas. Wu and Liu (2012) proposed adaptively weighted large margin classifiers for robust performance of classification in the presence of outliers. In this presentation we describe how the idea of adaptive classifiers can be used in the Sufficient Dimension Reduction framework to improve the performance of PSVM with outliers.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please 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.
Copyright © American Statistical Association.