ISyE Seminar - Helen Zhang

Event Details
  • Date/Time:
    • Wednesday November 28, 2018
      1:30 pm - 2:30 pm
  • Location: Groseclose 402
  • Phone:
  • URL: ISyE Building Complex
  • Email:
  • Fee(s):
    N/A
  • Extras:
Contact
No contact information submitted.
Summaries

Summary Sentence: Hierarchy-preserving regularization solution paths for identifying interactions in high dimensional data

Full Summary: Abstract:
Interaction screening for high-dimensional settings has recently drawn much attention in the literature. A variety of interaction screening approaches have been proposed for regression and classification problems. However, most of existing regularization methods for interaction selections are limited to low or moderate dimensional data analysis, due to their complex programing with inequality constraints and demanded prohibitive storage and computational cost when handling high dimensional data. This talk will present our recent work on scalable regularization methods to
interaction selection under hierarchical constraints for high dimensional regression and classification. We first consider two-stage LASSO methods and establish their theoretical properties. Then a new regularization method, called
Regularization Algorithm under Marginality Principle (RAMP), is developed to compute hierarchy-preserving regularization solution paths efficiently. In contrast to existing regularization methods, the proposed methods avoid
storing the entire design matrix and sidestep complex constraints and penalties, making them feasible to ultra-high dimensional data analysis. The new methods are further extended to handling binary responses. Extensive numerical results will be presented as well.

Abstract:
Interaction screening for high-dimensional settings has recently drawn much attention in the literature. A variety of interaction screening approaches have been proposed for regression and classification problems. However, most of existing regularization methods for interaction selections are limited to low or moderate dimensional data analysis, due to their complex programing with inequality constraints and demanded prohibitive storage and computational cost when handling high dimensional data. This talk will present our recent work on scalable regularization methods to
interaction selection under hierarchical constraints for high dimensional regression and classification. We first consider two-stage LASSO methods and establish their theoretical properties. Then a new regularization method, called
Regularization Algorithm under Marginality Principle (RAMP), is developed to compute hierarchy-preserving regularization solution paths efficiently. In contrast to existing regularization methods, the proposed methods avoid
storing the entire design matrix and sidestep complex constraints and penalties, making them feasible to ultra-high dimensional data analysis. The new methods are further extended to handling binary responses. Extensive numerical results will be presented as well.

 

About Me:    

Hao Helen Zhang
Department of Mathematics
University of Arizona, Tucson, AZ 85721
Email: hzhang@math.arizona.edu

http://math.arizona.edu/~hzhang/

Additional Information

In Campus Calendar
Yes
Groups

H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

Invited Audience
Faculty/Staff, Public, Graduate students, Undergraduate students
Categories
Conference/Symposium
Keywords
No keywords were submitted.
Status
  • Created By: nhendricks6
  • Workflow Status: Published
  • Created On: Aug 24, 2018 - 12:29pm
  • Last Updated: Oct 15, 2018 - 4:32pm