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 #312183
|
View Presentation
|
Title:
|
Sparse Canonical Correlation Analysis with General Covariance Structure via Convex Optimization
|
Author(s):
|
Irina Gaynanova*+ and James Booth and Martin Wells
|
Companies:
|
Cornell University and Cornell University and Cornell University
|
Keywords:
|
Canonical Correlation Analysis ;
Convex Optimization ;
Sparsity
|
Abstract:
|
Canonical Correlation Analysis (CCA) is a standard multivariate analysis tool that is used to find linear combinations of two sets of features with the maximum correlation. Its use for the modern high-dimensional data sets is challenging as it is usually of interest to select only a small subset of variables. Consequently, several methods for sparse CCA have been proposed in the literature. Although these methods have performed well in many applications, their main drawbacks are disregard of the covariance structure and lack of theoretical justification. Moreover, most of them require solving non-convex optimization problem and so convergence to the global solution is not guaranteed. In this paper we propose a novel sparse CCA method that overcomes these drawbacks by simultaneously estimating sparse linear combinations of features of both data sets. In addition, we put no assumptions on the underlying covariance structure. The convexity of the resulting optimization problem allows us to use computationally efficient algorithms to find the global solution. We compare our method with previous sparse CCA proposals via simulations and real data applications.
|
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.
Copyright © American Statistical Association.