Deflation based nonlinear canonical correlation analysis
dc.contributor.author | sharma, sanjay | |
dc.contributor.author | Kruger, U | |
dc.contributor.author | Irwin, GW | |
dc.date.accessioned | 2017-02-15T16:36:42Z | |
dc.date.available | 2017-02-15T16:36:42Z | |
dc.date.issued | 2006-01-01 | |
dc.identifier.issn | 0169-7439 | |
dc.identifier.issn | 1873-3239 | |
dc.identifier.uri | http://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.extent | 34-43 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Elsevier BV | |
dc.subject | nonlinear canonical correlation analysis | |
dc.subject | deflation procedure | |
dc.subject | linear projections | |
dc.subject | nonlinear transformations | |
dc.subject | neural networks | |
dc.subject | analysing variable interrelations | |
dc.title | Deflation based nonlinear canonical correlation analysis | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000238515300004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 1 | |
plymouth.volume | 83 | |
plymouth.publication-status | Published | |
plymouth.journal | Chemometrics and Intelligent Laboratory Systems | |
dc.identifier.doi | 10.1016/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 | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
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 | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dc.identifier.eissn | 1873-3239 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1016/j.chemolab.2005.12.008 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Journal Article/Review |