Kernel Penalized K-means: A feature selection method based on Kernel K-means
Author
dc.contributor.author
Maldonado Alarcón, Sebastián Alejandro
Author
dc.contributor.author
Carrizosa P., Emilio
Author
dc.contributor.author
Weber, Richard
Admission date
dc.date.accessioned
2015-12-09T01:58:58Z
Available date
dc.date.available
2015-12-09T01:58:58Z
Publication date
dc.date.issued
2015
Cita de ítem
dc.identifier.citation
Information Sciences 322 (2015) 150–160
en_US
Identifier
dc.identifier.other
DOI: 10.1016/j.ins.2015.06.008
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/135522
General note
dc.description
Artículo de publicación ISI
en_US
Abstract
dc.description.abstract
We present an unsupervised method that selects the most relevant features using an embedded strategy while maintaining the cluster structure found with the initial feature set. It is based on the idea of simultaneously minimizing the violation of the initial cluster structure and penalizing the use of features via scaling factors. As the base method we use Kernel K-means which works similarly to K-means, one of the most popular clustering algorithms, but it provides more flexibility due to the use of kernel functions for distance calculation, thus allowing the detection of more complex cluster structures. We present an algorithm to solve the respective minimization problem iteratively, and perform experiments with several data sets demonstrating the superior performance of the proposed method compared to alternative approaches.
en_US
Patrocinador
dc.description.sponsorship
FONDECYT project
11121196
1140831
Complex Engineering Systems Institute
ICM: P-05-004-F
CONICYT: FB016
Ministerio de Economia y Competitividad
MTM2012-36163-C06-03
Junta de Andalucia
P11-FQM-7603
FQM 329