title: Regularized greedy column subset selection
creator: Ordozgoiti Rubio, Bruno
creator: Mozo Velasco, Bonifacio Alberto
creator: Garcia Lopez De Lacalle, Jesus
subject: Computer Science
description: The Column Subset Selection Problem is a hard combinatorial optimization problem that provides a natural framework for unsupervised feature selection, and there exist efficient algorithms that provide good approximations. The drawback of the problem formulation is that it incorporates no form of regularization, and is therefore very sensitive to noise when presented with scarce data. In this paper we propose a regularized formulation of this problem, and derive a correct greedy algorithm that is similar in efficiency to existing greedy methods for the unregularized problem. We study its adequacy for feature selection and propose suitable formulations. Additionally, we derive a lower bound for the error of the proposed problems. Through various numerical experiments on real and synthetic data, we demonstrate the significantly increased robustness and stability of our method, as well as the improved conditioning of its output, all while remaining efficient for practical use.
publisher: E.T.S.I de Sistemas Informáticos (UPM)
rights: https://creativecommons.org/licenses/by-nc-nd/3.0/es/
date: 2019-06
type: info:eu-repo/semantics/article
type: Article
source: Information Sciences, ISSN 0020-0255, 2019-06, Vol. 486
type: PeerReviewed
format: application/pdf
language: eng
relation: https://www.sciencedirect.com/science/article/abs/pii/S0020025519301495
rights: info:eu-repo/semantics/openAccess
relation: info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ins.2019.02.039
identifier: https://oa.upm.es/64356/