Abstract Details
Activity Number:
|
225
|
Type:
|
Topic Contributed
|
Date/Time:
|
Monday, August 5, 2013 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
Abstract - #308936 |
Title:
|
Nested Semi-Definite Cone Analysis with Application to Diffusion Tensor Image Data
|
Author(s):
|
Lingsong Zhang*+ and Sungkyu Jung
|
Companies:
|
Purdue University and University of Pittsburgh
|
Keywords:
|
Nested Cone Analysis ;
Principal Component Analysis ;
Diffusion Tensor Image ;
Object-oriented data analysis ;
Subspace learning ;
Nonnegative matrix factorization
|
Abstract:
|
Motivated by Diffusion Tensor Imaging, we propose a nested semi-definite cone analysis, which provides a series of approximations of different ranks to the original data. At each rank k, all of the approximations lie in a dimension k subspace, and also are semi-definite, which leads to better interpretation, compared to other existing methods. Extensive simulations will be used to compare the connections and differences between our method and existing methods. The merit of our method will be shown in the application of this method to a Diffusion Tensor Image data set.
|
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.