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dc.contributor.authorSharma, SK
dc.contributor.authorKruger, U
dc.contributor.authorIrwin, GW
dc.date.accessioned2017-02-15T16:36:42Z
dc.date.available2017-02-15T16:36:42Z
dc.date.issued2006-01-01
dc.identifier.issn0169-7439
dc.identifier.issn1873-3239
dc.identifier.urihttp://hdl.handle.net/10026.1/8498
dc.description.abstract

This paper introduces two new techniques for determining nonlinear canonical correlation coefficients between two variable sets. A genetic strategy is incorporated to determine these coefficients. Compared to existing methods for nonlinear canonical correlation analysis (NLCCA), the benefits here are that the nonlinear mapping requires fewer parameters to be determined, consequently a more parsimonious NLCCA model can be established which is therefore simpler to interpret. A further contribution of the paper is the investigation of a variety of nonlinear deflation procedures for determining the subsequent nonlinear canonical coefficients. The benefits of the new approaches presented are demonstrated by application to an example from the literature and to recorded data from an industrial melter process. These studies show the advantages of the new NLCCA techniques presented and suggest that a nonlinear deflation procedure should be considered. © 2006 Elsevier B.V. All rights reserved.

dc.format.extent34-43
dc.languageen
dc.language.isoen
dc.publisherElsevier BV
dc.subjectnonlinear canonical correlation analysis
dc.subjectdeflation procedure
dc.subjectlinear projections
dc.subjectnonlinear transformations
dc.subjectneural networks
dc.subjectanalysing variable interrelations
dc.titleDeflation based nonlinear canonical correlation analysis
dc.typejournal-article
dc.typeArticle
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000238515300004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue1
plymouth.volume83
plymouth.publisher-urlhttp://dx.doi.org/10.1016/j.chemolab.2005.12.008
plymouth.publication-statusPublished
plymouth.journalChemometrics and Intelligent Laboratory Systems
dc.identifier.doi10.1016/j.chemolab.2005.12.008
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
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plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Marine Institute
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dc.identifier.eissn1873-3239
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1016/j.chemolab.2005.12.008
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review


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