Abstract Details
Activity Number:
|
651
|
Type:
|
Contributed
|
Date/Time:
|
Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
|
Sponsor:
|
IMS
|
Abstract - #310348 |
Title:
|
Fast Stagewise Algorithms for Approximate Regularization Paths
|
Author(s):
|
Ryan Tibshirani*+
|
Companies:
|
|
Keywords:
|
stagewise ;
boosting ;
regularization path
|
Abstract:
|
Forward stagewise regression enjoys an interesting connection to the lasso: under some conditions, the path of estimates constructed by forward stagewise exactly coincides with the lasso path, as the step size goes to zero. Essentially the same equivalence holds outside of regression, for the minimization of arbitrary differentiable convex loss functions subject to an $\ell_1$ norm constraint. Stagewise estimates provide a useful approximation even when they do not match their $\ell_1$-constrained analogues, and are computationally appealing. In general, regularization can take many forms, beyond the $\ell_1$ norm and sparsity; in this talk, we extend the stagewise idea to general convex regularization problems.
|
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.