- Title
- Sparse logistic regression utilizing cardinality constraints and information criteria
- Creator
- Urrutia, Gabriel; Delgado, Ramón A.; Carvajal, Rodrigo; Katselis, Dimitrios; Agüero, Juan C.
- Relation
- 2016 IEEE Conference on Control Applications (CCA). Proceedings of the 2016 IEEE Conference on Control Applications (CCA) (Buenos Aires, Argentina 19-22 September, 2016) p. 798-803
- Publisher Link
- http://dx.doi.org/10.1109/CCA.2016.7587916
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2016
- Description
- 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.
- Subject
- regression analysis; biology computing; DNA; genetics; maximum likelihood estimation; optimisation
- Identifier
- http://hdl.handle.net/1959.13/1346776
- Identifier
- uon:29924
- Identifier
- ISBN:9781509007561
- Rights
- © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
- Language
- eng
- Full Text
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