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    Recent advances on the reduction and analysis of big and high-dimensional data

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    MAK-DISSERTATION-2018.pdf (11.13Mb)
    Date
    2018-04-06
    Author
    Mak, Simon Tsz Fung
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    Abstract
    In 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.
    URI
    http://hdl.handle.net/1853/59913
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    • Georgia Tech Theses and Dissertations [23877]
    • School of Industrial and Systems Engineering Theses and Dissertations [1457]

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