Data Representation for Learning and Information Fusion in Bioinformatics

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dc.contributor.advisorCzaja, Wojciechen_US
dc.contributor.authorRajapakse, Vinodh Nalinen_US
dc.contributor.departmentMathematicsen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2013-10-03T05:31:54Z
dc.date.available2013-10-03T05:31:54Z
dc.date.issued2013en_US
dc.description.abstractThis thesis deals with the rigorous application of nonlinear dimension reduction and data organization techniques to biomedical data analysis. The Laplacian Eigenmaps algorithm is representative of these methods and has been widely applied in manifold learning and related areas. While their asymptotic manifold recovery behavior has been well-characterized, the clustering properties of Laplacian embeddings with finite data are largely motivated by heuristic arguments. We develop a precise bound, characterizing cluster structure preservation under Laplacian embeddings. From this foundation, we introduce flexible and mathematically well-founded approaches for information fusion and feature representation. These methods are applied to three substantial case studies in bioinformatics, illustrating their capacity to extract scientifically valuable information from complex data.en_US
dc.identifier.urihttp://hdl.handle.net/1903/14492
dc.subject.pqcontrolledApplied mathematicsen_US
dc.subject.pqcontrolledBioinformaticsen_US
dc.subject.pquncontrolledclusteringen_US
dc.subject.pquncontrolleddata fusionen_US
dc.subject.pquncontrolleddimension reductionen_US
dc.subject.pquncontrolledLaplacian Eigenmapsen_US
dc.subject.pquncontrolledpharmacologyen_US
dc.subject.pquncontrolledsystems biologyen_US
dc.titleData Representation for Learning and Information Fusion in Bioinformaticsen_US
dc.typeDissertationen_US

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