Beyond cross-domain learning: Multiple-domain nonnegative matrix factorization

Handle URI:
http://hdl.handle.net/10754/563372
Title:
Beyond cross-domain learning: Multiple-domain nonnegative matrix factorization
Authors:
Wang, Jim Jing-Yan; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Traditional cross-domain learning methods transfer learning from a source domain to a target domain. In this paper, we propose the multiple-domain learning problem for several equally treated domains. The multiple-domain learning problem assumes that samples from different domains have different distributions, but share the same feature and class label spaces. Each domain could be a target domain, while also be a source domain for other domains. A novel multiple-domain representation method is proposed for the multiple-domain learning problem. This method is based on nonnegative matrix factorization (NMF), and tries to learn a basis matrix and coding vectors for samples, so that the domain distribution mismatch among different domains will be reduced under an extended variation of the maximum mean discrepancy (MMD) criterion. The novel algorithm - multiple-domain NMF (MDNMF) - was evaluated on two challenging multiple-domain learning problems - multiple user spam email detection and multiple-domain glioma diagnosis. The effectiveness of the proposed algorithm is experimentally verified. © 2013 Elsevier Ltd. All rights reserved.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Computational Bioscience Research Center (CBRC); Structural and Functional Bioinformatics Group
Publisher:
Elsevier BV
Journal:
Engineering Applications of Artificial Intelligence
Issue Date:
Feb-2014
DOI:
10.1016/j.engappai.2013.11.002
Type:
Article
ISSN:
09521976
Sponsors:
The study was supported by grants from Chongqing Key Laboratory of Computational Intelligence, China (Grant no. CQ-LCI-2013-02), Tianjin Key Laboratory of Cognitive Computing and Application, China, and King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
Appears in Collections:
Articles; Structural and Functional Bioinformatics Group; Computer Science Program; 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.authorGao, Xinen
dc.date.accessioned2015-08-03T11:46:56Zen
dc.date.available2015-08-03T11:46:56Zen
dc.date.issued2014-02en
dc.identifier.issn09521976en
dc.identifier.doi10.1016/j.engappai.2013.11.002en
dc.identifier.urihttp://hdl.handle.net/10754/563372en
dc.description.abstractTraditional cross-domain learning methods transfer learning from a source domain to a target domain. In this paper, we propose the multiple-domain learning problem for several equally treated domains. The multiple-domain learning problem assumes that samples from different domains have different distributions, but share the same feature and class label spaces. Each domain could be a target domain, while also be a source domain for other domains. A novel multiple-domain representation method is proposed for the multiple-domain learning problem. This method is based on nonnegative matrix factorization (NMF), and tries to learn a basis matrix and coding vectors for samples, so that the domain distribution mismatch among different domains will be reduced under an extended variation of the maximum mean discrepancy (MMD) criterion. The novel algorithm - multiple-domain NMF (MDNMF) - was evaluated on two challenging multiple-domain learning problems - multiple user spam email detection and multiple-domain glioma diagnosis. The effectiveness of the proposed algorithm is experimentally verified. © 2013 Elsevier Ltd. All rights reserved.en
dc.description.sponsorshipThe study was supported by grants from Chongqing Key Laboratory of Computational Intelligence, China (Grant no. CQ-LCI-2013-02), Tianjin Key Laboratory of Cognitive Computing and Application, China, and King Abdullah University of Science and Technology (KAUST), Saudi Arabia.en
dc.publisherElsevier BVen
dc.subjectCross-domain learningen
dc.subjectData representationen
dc.subjectNonnegative matrix factorizationen
dc.titleBeyond cross-domain learning: Multiple-domain nonnegative matrix factorizationen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentStructural and Functional Bioinformatics Groupen
dc.identifier.journalEngineering Applications of Artificial Intelligenceen
dc.contributor.institutionChongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, Chinaen
kaust.authorWang, Jim Jing-Yanen
kaust.authorGao, Xinen
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