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Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/5598
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dc.contributor.authorGuo, Yien
dc.contributor.authorGao, Junbinen
dc.contributor.authorKwan, Paul Hen
local.source.editorEditor(s): Ann Nicholson & Xiaodong Lien
dc.date.accessioned2010-04-16T10:15:00Z-
dc.date.issued2009-
dc.identifier.citationAI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference Melbourne, Australia, December 1-4, 2009 Proceedings, p. 240-249en
dc.identifier.isbn9783642104381en
dc.identifier.isbn364210438Xen
dc.identifier.urihttps://hdl.handle.net/1959.11/5598-
dc.description.abstractIn this paper, we proposed a new nonlinear dimensionality reduction algorithm called regularized Kernel Local Linear Embedding (rKLLE) for highly structured data. It is built on the original LLE by introducing kernel alignment type of constraint to effectively reduce the solution space and find out the embeddings reflecting the prior knowledge. To enable the non-vectorial data applicability of the algorithm, a kernelized LLE is used to get the reconstruction weights. Our experiments on typical non-vectorial data show that rKLLE greatly improves the results of KLLE.en
dc.languageenen
dc.publisherSpringeren
dc.relation.ispartofAI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference Melbourne, Australia, December 1-4, 2009 Proceedingsen
dc.relation.ispartofseriesLecture Notes in Computer Scienceen
dc.titleRegularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Dataen
dc.typeConference Publicationen
dc.relation.conferenceAI 2009: 22nd Australasian Joint Conference Melbourneen
dc.identifier.doi10.1007/978-3-642-10439-8_25en
dc.subject.keywordsPattern Recognition and Data Miningen
dc.subject.keywordsNeural, Evolutionary and Fuzzy Computationen
local.contributor.firstnameYien
local.contributor.firstnameJunbinen
local.contributor.firstnamePaul Hen
local.subject.for2008080109 Pattern Recognition and Data Miningen
local.subject.for2008080108 Neural, Evolutionary and Fuzzy Computationen
local.subject.seo2008890202 Application Tools and System Utilitiesen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailwkwan2@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20100414-16192en
local.date.conference1st - 4th December, 2009en
local.conference.placeMelbourne, Australiaen
local.publisher.placeBerlin, Germanyen
local.identifier.totalchapters68en
local.format.startpage240en
local.format.endpage249en
local.series.issn1611-3349-
local.series.issn0302-9743-
local.series.number5866en
local.contributor.lastnameGuoen
local.contributor.lastnameGaoen
local.contributor.lastnameKwanen
local.seriespublisherSpringeren
local.seriespublisher.placeBerlin, Germanyen
dc.identifier.staffune-id:wkwan2en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:5730en
dc.identifier.academiclevelAcademicen
local.title.maintitleRegularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Dataen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.conference.detailsAI 2009: 22nd Australasian Joint Conference Melbourne, Australia, 1st - 4th December, 2009en
local.search.authorGuo, Yien
local.search.authorGao, Junbinen
local.search.authorKwan, Paul Hen
local.uneassociationUnknownen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2009en
local.date.start2009-12-01-
local.date.end2009-12-04-
local.profile.affiliationtypeUnknownen
local.profile.affiliationtypeUnknownen
local.profile.affiliationtypeUnknownen
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