Dimensionality reduction for hyperspectral data

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dc.contributor.advisorBenedetto, John Jen_US
dc.contributor.advisorCzaja, Wojciechen_US
dc.contributor.authorWidemann, David Pen_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.accessioned2008-10-11T05:30:16Z
dc.date.available2008-10-11T05:30:16Z
dc.date.issued2008-05-09en_US
dc.description.abstractThis thesis is about dimensionality reduction for hyperspectral data. Special emphasis is given to dimensionality reduction techniques known as kernel eigenmap methods and manifold learning algorithms. Kernel eigenmap methods require a nearest neighbor or a radius parameter be set. A new algorithm that does not require these neighborhood parameters is given. Most kernel eigenmap methods use the eigenvectors of the kernel as coordinates for the data. An algorithm that uses the frame potential along with subspace frames to create nonorthogonal coordinates is given. The algorithms are demonstrated on hyperspectral data. The last two chapters include analysis of representation systems for LIDAR data and motion blur estimation, respectively.en_US
dc.format.extent6608724 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/8448
dc.language.isoen_US
dc.subject.pqcontrolledMathematicsen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pqcontrolledMathematicsen_US
dc.subject.pquncontrolleddimensionality reductionen_US
dc.subject.pquncontrolledkernel methodsen_US
dc.subject.pquncontrolledmanifold learningen_US
dc.subject.pquncontrolledframesen_US
dc.titleDimensionality reduction for hyperspectral dataen_US
dc.typeDissertationen_US

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