Sparse Logistic Regression utilizing Cardinality Constraints and Information Criteria

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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2016 IEEE Conference on Control Applications, CCA 2016
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas798-803
Número de páginas6
ISBN (versión digital)9781509007554
DOI
EstadoPublicada - 10 oct. 2016
Publicado de forma externa
Evento2016 IEEE Conference on Control Applications, CCA 2016 - Buenos Aires, Argentina
Duración: 19 sept. 201622 sept. 2016

Serie de la publicación

Nombre2016 IEEE Conference on Control Applications, CCA 2016

Conferencia

Conferencia2016 IEEE Conference on Control Applications, CCA 2016
País/TerritorioArgentina
CiudadBuenos Aires
Período19/09/1622/09/16

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