Abstract Details
Activity Number:
|
299
|
Type:
|
Topic Contributed
|
Date/Time:
|
Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Biometrics Section
|
Abstract - #307832 |
Title:
|
High-Dimensional Learning for Ordinal and Multiclass Data
|
Author(s):
|
Xingye Qiao*+
|
Companies:
|
Binghamton University
|
Keywords:
|
Fisher consistency ;
High-dimensional, low-sample size data ;
Multiclass classification ;
Non-crossing constraints ;
Ordinal classification ;
Statistical learning
|
Abstract:
|
In this talk, I will discuss two projects. In the first project, the goal is to classify ordinal data, which have class labels from a set of categories with inherent orders. We propose a non-crossing ordinal classifier where multiple binary ones are trained simultaneously with a constraint that rules out crossing phenomena among their classification boundaries. In the second project, the aim is to conduct variable selection for multiclass classification. With the state-of-the-art Lasso type of penalization, the resulting variable selection method actually selects coefficients instead of variables. We use three different group penalties to conduct true variable selection for multiclass classification.
|
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.