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    A Comparison of Some Dimension Reduction Techniques with Varied Parameters

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    Oderio_ku_0099M_15662_DATA_1.pdf (1.289Mb)
    Issue Date
    2017-13-31
    Author
    Oderio, Nicholas
    Publisher
    University of Kansas
    Format
    46 pages
    Type
    Thesis
    Degree Level
    M.A.
    Discipline
    Mathematics
    Rights
    Copyright held by the author.
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    Abstract
    This paper presents and explains several methods of dimensionality reduction of data sets, beginning with the well known PCA and moving onto techniques that deal with data on a nonlinear manifold. Methods for handling data whose underlying structure is a nonlinear manifold are separated by whether or not sparse matrices are involved in the computation. Additionally, the methods discussed are demonstrated and compared by running them on data sets whose underlying structure is known. Results from same methods with different values for input parameters are also examined. Finally, some results on a small set of Persyst EEG data collected as a part of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy from the Laboratory of Neuro Imaging at USC Stevens Institute of Neuroimaging and Informatics in the Keck School of Medicine of USC is analyzed using some of these methods.
    URI
    http://hdl.handle.net/1808/26902
    Collections
    • Mathematics Dissertations and Theses [179]
    • Theses [3828]

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    785-864-8983
    KU Libraries
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    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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