GRAPH-BASED DATA FUSION WITH APPLICATIONS TO MAGNETIC RESONANCE IMAGING

dc.contributor.advisorCzaja, Wojciechen_US
dc.contributor.authorEmidih, Jeremiahen_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.accessioned2024-02-14T06:42:32Z
dc.date.available2024-02-14T06:42:32Z
dc.date.issued2023en_US
dc.description.abstractThis thesis is concerned with development and applications of data fusion methods in thecontext of Laplacian eigenmaps. Multimodal data can be challenging to work with using classical statistical and signal processing techniques. Graphs provide a reference frame for the study of otherwise structure-less data. We combine spectral methods on graphs and geometric data analysis in order to create a novel data fusion model. We also provide examples of applications of this model to bioinformatics, color transformation and superresolution, and magnetic resonance imaging.en_US
dc.identifierhttps://doi.org/10.13016/ynfs-jq20
dc.identifier.urihttp://hdl.handle.net/1903/31743
dc.language.isoenen_US
dc.subject.pqcontrolledMathematicsen_US
dc.subject.pqcontrolledApplied mathematicsen_US
dc.titleGRAPH-BASED DATA FUSION WITH APPLICATIONS TO MAGNETIC RESONANCE IMAGINGen_US
dc.typeDissertationen_US

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