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dc.contributor.advisorPeter, Adrian
dc.contributor.authorWinsala, Oluwaseun Ebenezer
dc.date.accessioned2017-01-06T19:16:41Z
dc.date.available2017-01-06T19:16:41Z
dc.date.issued2016-12
dc.identifier.urihttp://hdl.handle.net/11141/1123
dc.descriptionThesis (M.S.) - Florida Institute of Technology, 2016en_US
dc.description.abstractThis thesis presents a novel framework for time series classification that leverages the geometric structure of covariance matrices when labeling signals. Our method maps each signal to a new multivariate localized feature signal (MLFS) representation, from which we compute a covariance descriptor. This robust MLFS covarieance representation handles classification tasks where the sampling rates of the signals vary within a class or classes. We demonstrate that by simply using the k-nearest neighbor classification rule and multiclass kernel support vector machine with the Riemannian metric between the MLFS convariance matrices, which produces state-of-the-art results on a number of standard datasets. Moreover, for the first time, we showcase results on the full library of typical infrasonic signals dataset, which contains four categories of infrasound observations.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.rightsCopyright held by author.en_US
dc.titleTime Series Classification using Covariance Descriptorsen_US
dc.typeThesisen_US
dc.date.updated2017-01-04T16:33:04Z
thesis.degree.nameMaster of Science in Systems Engineeringen_US
thesis.degree.levelMastersen_US
thesis.degree.disciplineSystems Engineeringen_US
thesis.degree.departmentEngineering Systemsen_US
thesis.degree.grantorFlorida Institute of Technologyen_US
dc.type.materialtext


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