The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
|
Activity Number:
|
451
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
| Abstract - #300641 |
|
Title:
|
Principal Subspace Estimation in Spiked Covariance Models
|
|
Author(s):
|
Zongming Ma*+
|
|
Companies:
|
University of Pennsylvania
|
|
Address:
|
Department of Statistics, Philadelphia, PA, 19104,
|
|
Keywords:
|
high dimension ;
principal component analysis ;
sparsity ;
spiked covariance model ;
subspace
|
|
Abstract:
|
For high-dimensional data, it is often desirable to reduce the dimensionality by projection onto a low dimensional principal subspace. However, classical PCA usually cannot find the subspace consistently in high dimensions. In this talk, we present a new principal subspace estimation method. For a class of spiked covariance models with sparsity constraints, it consistently, and even optimally, estimates the subspace.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2011 program
|
2011 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.