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dc.contributor.advisorVengazhiyil, Roshan J.
dc.contributor.advisorWu, C. F. Jeff
dc.contributor.authorMak, Simon Tsz Fung
dc.date.accessioned2018-05-31T18:16:05Z
dc.date.available2018-05-31T18:16:05Z
dc.date.created2018-05
dc.date.issued2018-04-06
dc.date.submittedMay 2018
dc.identifier.urihttp://hdl.handle.net/1853/59913
dc.description.abstractIn an era with remarkable advancements in computer engineering, computational algorithms, and mathematical modeling, data scientists are inevitably faced with the challenge of working with big and high-dimensional data. For many problems, data reduction is a necessary first step; such reduction allows for storage and portability of big data, and enables the computation of expensive downstream quantities. The next step then involves the analysis of big data -- the use of such data for modeling, inference, and prediction. This thesis presents new methods for big data reduction and analysis, with a focus on solving real-world problems in statistics, machine learning and engineering.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectBig data
dc.subjectHigh-dimensional statistics
dc.subjectData reduction
dc.subjectMachine learning
dc.subjectVariable selection
dc.subjectComputer experiments
dc.subjectExperimental design
dc.titleRecent advances on the reduction and analysis of big and high-dimensional data
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentIndustrial and Systems Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberXie, Yao
dc.contributor.committeeMemberLan, George
dc.contributor.committeeMemberHickernell, Fred J.
dc.date.updated2018-05-31T18:16:05Z


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