JSM 2014 Home
Online Program Home
My Program

Abstract Details

Activity Number: 39
Type: Contributed
Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #312479 View Presentation
Title: Dimension Reduction Using Inverse Spline Regression
Author(s): Paul Smith*+ and Kijoeng Nam
Companies: University of Maryland and University of Maryland
Keywords: High-dimensional data ; Inverse regression ; Asymptotics
Abstract:

In high-dimensional data analysis, we often want to reduce the number of pre- dictors without eliminating variables which are related to the response of interest. Inverse regression methods use the response variable when performing dimension reduction so that information regarding the relation between the covariates and the response is not lost. However, it is common to assume that the inverse regression function is linear or to use some other ad hoc approach. Instead, we propose a new dimension reduction method which models the inverse regression function as a spline. We develop asymptotics for our approach and demonstrate its performance through simulations and several data sets commonly found in the machine learning literature. We show that its performance is better than existing inverse regression based methods, especially when the dimension reduction space is a nonlinear manifold such as the Swiss roll example of Roweis and Saul (2000).


Authors who are presenting talks have a * after their name.

Back to the full JSM 2014 program




2014 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Professional Development 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.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.