Coordinate Descent Methods for Sparse Optimal Scoring and its Applications

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dc.contributorHalpern, David
dc.contributorMalaia, Evguenia
dc.contributorWang, Chuntian
dc.contributorZhu, Wei
dc.contributor.advisorAmes, Brendan P
dc.contributor.authorFord, Katie Wood
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2021-11-23T14:33:47Z
dc.date.available2021-11-23T14:33:47Z
dc.date.issued2021
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractLinear discriminant analysis (LDA) is a popular tool for performing supervised classification in a high-dimensional setting. It seeks to reduce the dimension by projecting the data to a lower dimensional space using a set of optimal discriminant vectors to separate the classes. One formulation of LDA is optimal scoring which uses a sequence of scores to turn the categorical variables into quantitative variables. In this way, optimal scoring creates a generalized linear regression problem from a classification problem. The sparse optimal scoring formulation of LDA uses an elastic-net penalty on the discriminant vectors to induce sparsity and perform feature selection. We propose coordinate descent algorithms for finding optimal discriminant vectors in the sparse optimal scoring formulation of LDA, along with parallel implementations for large-scale problems. We then present numerical results illustrating the efficacy of these algorithms in classifying real and simulated data. Finally, we use Sparse Optimal Scoring to analyze and classify visual comprehension of Deaf persons based on EEG data.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otherhttp://purl.lib.ua.edu/181441
dc.identifier.otheru0015_0000001_0003880
dc.identifier.otherFord_alatus_0004D_14537
dc.identifier.urihttp://ir.ua.edu/handle/123456789/8112
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.subjectCoordinate Descent
dc.subjectLinear Discriminant Analysis
dc.subjectSparse Optimal Scoring
dc.titleCoordinate Descent Methods for Sparse Optimal Scoring and its Applicationsen_US
dc.typethesis
dc.typetext
etdms.degree.departmentUniversity of Alabama. Department of Mathematics
etdms.degree.disciplineApplied mathematics
etdms.degree.grantorThe University of Alabama
etdms.degree.leveldoctoral
etdms.degree.namePh.D.
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