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Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/4513
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dc.contributor.authorGuo, Yien
dc.contributor.authorGao, Junbinen
dc.contributor.authorKwan, Paul Hingen
local.source.editorEditor(s): Orgun, Mehmet A, Thornton, Jen
dc.date.accessioned2010-02-05T16:15:00Z-
dc.date.issued2007-
dc.identifier.citationAI 2007: Advances in Artificial Intelligence: Proceedings of the 20th Australian Joint Conference on Artificial Intelligence Gold Coast, Australia, December 2-6, 2007, p. 659-663en
dc.identifier.isbn9783540769262en
dc.identifier.urihttps://hdl.handle.net/1959.11/4513-
dc.description.abstractThis paper proposes a new nonlinear dimensionality reduction algorithm called RCTKE for highly structured data. It is built on the original TKE by incorporating a mapping function into the objective functional of TKE as regularization terms where the mapping function can be learned from training data and be used for novel samples. The experimental results on highly structured data is used to verify the effectiveness of the algorithm.en
dc.languageenen
dc.publisherSpringeren
dc.relation.ispartofAI 2007: Advances in Artificial Intelligence: Proceedings of the 20th Australian Joint Conference on Artificial Intelligence Gold Coast, Australia, December 2-6, 2007en
dc.titleTwin Kernel Embedding with Relaxed Constraints on Dimensionality Reduction for Structured Dataen
dc.typeConference Publicationen
dc.relation.conferenceAI 2007: 20th Australian Joint Conference on Artificial Intelligenceen
dc.identifier.doi10.1007/978-3-540-76928-6_71en
dc.subject.keywordsPattern Recognition and Data Miningen
local.contributor.firstnameYien
local.contributor.firstnameJunbinen
local.contributor.firstnamePaul Hingen
local.subject.for2008080109 Pattern Recognition and Data Miningen
local.subject.seo2008890201 Application Software Packages (excl. Computer Games)en
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailyguo4@une.edu.auen
local.profile.emailjgao@une.edu.auen
local.profile.emailwkwan2@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordpes:5537en
local.date.conference2nd - 6th December, 2007en
local.conference.placeGold Coast, Australiaen
local.publisher.placeBerlin, Germanyen
local.format.startpage659en
local.format.endpage663en
local.peerreviewedYesen
local.contributor.lastnameGuoen
local.contributor.lastnameGaoen
local.contributor.lastnameKwanen
dc.identifier.staffune-id:yguo4en
dc.identifier.staffune-id:jgaoen
dc.identifier.staffune-id:wkwan2en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:4620en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleTwin Kernel Embedding with Relaxed Constraints on Dimensionality Reduction for Structured Dataen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.conference.detailsAI 2007: 20th Australian Joint Conference on Artificial Intelligence, Australia, December 2-6, 2007, Gold Coast, Australia, 2 - 6 December, 2007en
local.search.authorGuo, Yien
local.search.authorGao, Junbinen
local.search.authorKwan, Paul Hingen
local.uneassociationUnknownen
local.year.published2007en
local.date.start2007-12-02-
local.date.end2007-12-06-
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