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dc.contributor.authorGhodsi Boushehri, Alien
dc.date.accessioned2006-08-22 14:26:07 (GMT)
dc.date.available2006-08-22 14:26:07 (GMT)
dc.date.issued2006en
dc.date.submitted2006en
dc.identifier.urihttp://hdl.handle.net/10012/1020
dc.description.abstractIn this thesis, I look at three problems with important applications in data processing. Incorporating side information, provided by the user or derived from data, is a main theme of each of these problems. <br /><br /> This thesis makes a number of contributions. The first is a technique for combining different embedding objectives, which is then exploited to incorporate side information expressed in terms of transformation invariants known to hold in the data. It also introduces two different ways of incorporating transformation invariants in order to make new similarity measures. Two algorithms are proposed which learn metrics based on different types of side information. These learned metrics can then be used in subsequent embedding methods. Finally, it introduces a manifold learning algorithm that is useful when applied to sequential decision problems. In this case we are given action labels in addition to data points. Actions in the manifold learned by this algorithm have meaningful representations in that they are represented as simple transformations.en
dc.formatapplication/pdfen
dc.format.extent6812048 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.rightsCopyright: 2006, Ghodsi Boushehri, Ali. All rights reserved.en
dc.subjectMathematicsen
dc.subjectStatisticsen
dc.subjectComputer Scienceen
dc.subjectArtificial intelligenceen
dc.subjectMachine learningen
dc.subjectDimensionality reductionen
dc.subjectManifold learningen
dc.subjectUnsupervised learningen
dc.subjectHigh dimensional dataen
dc.titleNonlinear Dimensionality Reduction with Side Informationen
dc.typeDoctoral Thesisen
dc.pendingfalseen
uws-etd.degree.departmentSchool of Computer Scienceen
uws-etd.degreeDoctor of Philosophyen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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