COMPUTATIONAL METHODS IN MACHINE LEARNING: TRANSPORT MODEL, HAAR WAVELET, DNA CLASSIFICATION, AND MRI

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dc.contributor.advisorCzaja, Wojciech Ken_US
dc.contributor.advisorBenedetto, John Jen_US
dc.contributor.authorNjeunje, Franck Olivier Ndjakouen_US
dc.contributor.departmentApplied Mathematics and Scientific Computationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2018-09-07T05:35:06Z
dc.date.available2018-09-07T05:35:06Z
dc.date.issued2018en_US
dc.description.abstractWith the increasing amount of raw data generation produced every day, it has become pertinent to develop new techniques for data representation, analyses, and interpretation. Motivated by real-world applications, there is a trending interest in techniques such as dimensionality reduction, wavelet decomposition, and classication methods that allow for better understanding of data. This thesis details the development of a new non-linear dimension reduction technique based on transport model by advection. We provide a series of computational experiments, and practical applications in hyperspectral images to illustrate the strength of our algorithm. In wavelet decomposition, we construct a novel Haar approximation technique for functions f in the Lp-space, 0 < p < 1, such that the approximants have support contained in the support of f. Furthermore, a classification algorithm to study tissue-specific deoxyribonucleic acids (DNA) is constructed using the support vector machine. In magnetic resonance imaging, we provide an extension of the T2-store-T2 magnetic resonance relaxometry experiment used in the analysis of magnetization signal from 2 to N exchanging sites, where N >= 2.en_US
dc.identifierhttps://doi.org/10.13016/M2NK36832
dc.identifier.urihttp://hdl.handle.net/1903/21132
dc.language.isoenen_US
dc.subject.pqcontrolledApplied mathematicsen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledClassificationen_US
dc.subject.pquncontrolledData representationen_US
dc.subject.pquncontrolledDimension reductionen_US
dc.subject.pquncontrolledMachine Learninen_US
dc.subject.pquncontrolledUnsupervised Learningen_US
dc.subject.pquncontrolledWavelet decompositionen_US
dc.titleCOMPUTATIONAL METHODS IN MACHINE LEARNING: TRANSPORT MODEL, HAAR WAVELET, DNA CLASSIFICATION, AND MRIen_US
dc.typeDissertationen_US

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