Regularized maximum correntropy machine

Handle URI:
http://hdl.handle.net/10754/577311
Title:
Regularized maximum correntropy machine
Authors:
Wang, Jim Jing-Yan; Wang, Yunji; Jing, Bing-Yi; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we regularize the predictor parameter to control the complexity of the predictor. The learning problem is formulated by an objective function considering the parameter regularization and MCC simultaneously. By optimizing the objective function alternately, we develop a novel predictor learning algorithm. The experiments on two challenging pattern classification tasks show that it significantly outperforms the machines with transitional loss functions.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Regularized maximum correntropy machine 2015, 160:85 Neurocomputing
Publisher:
Elsevier BV
Journal:
Neurocomputing
Issue Date:
12-Feb-2015
DOI:
10.1016/j.neucom.2014.09.080
Type:
Article
ISSN:
09252312
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S0925231215001150
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Jim Jing-Yanen
dc.contributor.authorWang, Yunjien
dc.contributor.authorJing, Bing-Yien
dc.contributor.authorGao, Xinen
dc.date.accessioned2015-09-15T07:15:42Zen
dc.date.available2015-09-15T07:15:42Zen
dc.date.issued2015-02-12en
dc.identifier.citationRegularized maximum correntropy machine 2015, 160:85 Neurocomputingen
dc.identifier.issn09252312en
dc.identifier.doi10.1016/j.neucom.2014.09.080en
dc.identifier.urihttp://hdl.handle.net/10754/577311en
dc.description.abstractIn this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we regularize the predictor parameter to control the complexity of the predictor. The learning problem is formulated by an objective function considering the parameter regularization and MCC simultaneously. By optimizing the objective function alternately, we develop a novel predictor learning algorithm. The experiments on two challenging pattern classification tasks show that it significantly outperforms the machines with transitional loss functions.en
dc.language.isoenen
dc.publisherElsevier BVen
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0925231215001150en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, 12 February 2015. DOI: 10.1016/j.neucom.2014.09.080en
dc.subjectPattern classificationen
dc.subjectLabel noiseen
dc.subjectMaximum Correntropy Criteriaen
dc.subjectRegularizationen
dc.titleRegularized maximum correntropy machineen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalNeurocomputingen
dc.eprint.versionPost-printen
dc.contributor.institutionElectrical and Computer Engineering Department, The University of Texas at San Antonio, San Antonio, TX 78249, USAen
dc.contributor.institutionDepartment of Mathematics, Hong Kong University of Science and Technology, Kowloon, Hong Kongen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorGao, Xinen
kaust.authorWang, Jim Jing-Yanen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.