Non-negative matrix factorization by maximizing correntropy for cancer clustering

Handle URI:
http://hdl.handle.net/10754/325473
Title:
Non-negative matrix factorization by maximizing correntropy for cancer clustering
Authors:
Wang, Jim Jing-Yan; Wang, Xiaolei; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Background: Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l2 norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise.Results: We propose a maximum correntropy criterion (MCC)-based NMF method (NMF-MCC) for gene expression data-based cancer clustering. Instead of minimizing the l2 norm or the Kullback-Leibler distance, NMF-MCC maximizes the correntropy between the product of the two matrices and the original matrix. The optimization problem can be solved by an expectation conditional maximization algorithm.Conclusions: Extensive experiments on six cancer benchmark sets demonstrate that the proposed method is significantly more accurate than the state-of-the-art methods in cancer clustering. 2013 Wang et al.; licensee BioMed Central Ltd.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computational Bioscience Research Center (CBRC)
Citation:
Wang JJ-Y, Wang X, Gao X (2013) Non-negative matrix factorization by maximizing correntropy for cancer clustering. BMC Bioinformatics 14: 107. doi:10.1186/1471-2105-14-107.
Publisher:
Springer Nature
Journal:
BMC Bioinformatics
Issue Date:
24-Mar-2013
DOI:
10.1186/1471-2105-14-107
PubMed ID:
23522344
PubMed Central ID:
PMC3659102
Type:
Article
ISSN:
14712105
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Jim Jing-Yanen
dc.contributor.authorWang, Xiaoleien
dc.contributor.authorGao, Xinen
dc.date.accessioned2014-08-27T09:52:54Z-
dc.date.available2014-08-27T09:52:54Z-
dc.date.issued2013-03-24en
dc.identifier.citationWang JJ-Y, Wang X, Gao X (2013) Non-negative matrix factorization by maximizing correntropy for cancer clustering. BMC Bioinformatics 14: 107. doi:10.1186/1471-2105-14-107.en
dc.identifier.issn14712105en
dc.identifier.pmid23522344en
dc.identifier.doi10.1186/1471-2105-14-107en
dc.identifier.urihttp://hdl.handle.net/10754/325473en
dc.description.abstractBackground: Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l2 norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise.Results: We propose a maximum correntropy criterion (MCC)-based NMF method (NMF-MCC) for gene expression data-based cancer clustering. Instead of minimizing the l2 norm or the Kullback-Leibler distance, NMF-MCC maximizes the correntropy between the product of the two matrices and the original matrix. The optimization problem can be solved by an expectation conditional maximization algorithm.Conclusions: Extensive experiments on six cancer benchmark sets demonstrate that the proposed method is significantly more accurate than the state-of-the-art methods in cancer clustering. 2013 Wang et al.; licensee BioMed Central Ltd.en
dc.language.isoenen
dc.publisherSpringer Natureen
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en
dc.subjectExpectation conditional maximizationsen
dc.subjectGene Expression Dataen
dc.subjectKullback-Leibler distanceen
dc.subjectNon-negative matrixen
dc.subjectNonnegative matrix factorizationen
dc.subjectOptimization problemsen
dc.subjectSimilarity measurementsen
dc.subjectState-of-the-art methodsen
dc.subjectDiseasesen
dc.subjectFilter banksen
dc.subjectGene expressionen
dc.subjectMatrix algebraen
dc.subjectFactorizationen
dc.subjectalgorithmen
dc.subjectclassificationen
dc.subjectcluster analysisen
dc.subjectgene expression profilingen
dc.subjectgeneticsen
dc.subjectmetabolismen
dc.subjectmethodologyen
dc.subjectneoplasmen
dc.subjectAlgorithmsen
dc.subjectCluster Analysisen
dc.subjectGene Expression Profilingen
dc.subjectNeoplasmsen
dc.titleNon-negative matrix factorization by maximizing correntropy for cancer clusteringen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalBMC Bioinformaticsen
dc.identifier.pmcidPMC3659102en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionUnidad Académica de Sistemas Arrecifales (Puerto Morelos), Instituto de Ciencias Del Mar y Limnología, Universidad Nacional Autõnoma de México, Puerto Morelos, QR 77580, Mexicoen
dc.contributor.institutionSchool of Natural Sciences, University of California Merced, 5200 North Lake Road, Merced, CA 95343, United Statesen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorWang, Jim Jing-Yanen
kaust.authorGao, Xinen
kaust.authorWang, Xiaoleien
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