Sparse Logistic Regression utilizing Cardinality Constraints and Information Criteria

Gabriel Urrutia, Ramon Delgado, Rodrigo Carvajal, Dimitrios Katselis, Juan C. Agüero

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we address the problem of estimating a sparse parameter vector that defines a logistic regression. The problem is then solved using two approaches: i) inequality constrained Maximum Likelihood estimation and ii) penalized Maximum Likelihood which is closely related to Information Criteria such as AIC. For the promotion of sparsity, we utilize a nonlinear constraint based on the ℓ0 (pseudo) norm of the parameter vector. The corresponding optimization problem is solved using an equivalent representation of the problem that is simpler to solve. We illustrate the benefits of our proposal with an example that is inspired by a gene selection problem in DNA microarrays.

Original languageEnglish (US)
Title of host publication2016 IEEE Conference on Control Applications, CCA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages798-803
Number of pages6
ISBN (Electronic)9781509007554
DOIs
StatePublished - Oct 10 2016
Externally publishedYes
Event2016 IEEE Conference on Control Applications, CCA 2016 - Buenos Aires, Argentina
Duration: Sep 19 2016Sep 22 2016

Publication series

Name2016 IEEE Conference on Control Applications, CCA 2016

Other

Other2016 IEEE Conference on Control Applications, CCA 2016
Country/TerritoryArgentina
CityBuenos Aires
Period9/19/169/22/16

ASJC Scopus subject areas

  • Control and Optimization
  • Modeling and Simulation

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