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dc.contributor.advisorZhang, Hao Helen
dc.contributor.authorOuyang, Wenbo
dc.creatorOuyang, Wenbo
dc.date.accessioned2023-09-14T08:39:12Z
dc.date.available2023-09-14T08:39:12Z
dc.date.issued2023
dc.identifier.citationOuyang, Wenbo. (2023). Dynamic Supervised Principal Component Analysis for Classification (Doctoral dissertation, University of Arizona, Tucson, USA).
dc.identifier.urihttp://hdl.handle.net/10150/669828
dc.description.abstractContemporary research places great importance on high-dimensional classification, with dynamic classification problems being of particular interest. Such problems involve situations where the distributions of both classes are not static and change with time or other index variables. This paper proposes a new framework in the context of linear discriminant analysis (LDA) for learning classification decision rules that can adapt to changes with respect to the index variable. Furthermore, many existing works on high-dimensional classification problems make the sparsity assumption about the original feature space, which may not hold in practice. Our framework relaxes this assumption by learning the hidden sparse structure of the data through data rotation. In this work, we propose a new dimension reduction method in the context of dynamic problems. The new method employs a kernel smoothing procedure to determine the suitable direction for dimension reduction. Numerical simulations and real data examples are illustrated to demonstrate the performance of the new approach in terms of both classification accuracy and computational efficiency. One extension to solve non-normal data problems is also included.
dc.language.isoen
dc.publisherThe University of Arizona.
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.titleDynamic Supervised Principal Component Analysis for Classification
dc.typeElectronic Dissertation
dc.typetext
thesis.degree.grantorUniversity of Arizona
thesis.degree.leveldoctoral
dc.contributor.committeememberHao, Ning
dc.contributor.committeememberNiu, Yue
dc.contributor.committeememberAn, Lingling
thesis.degree.disciplineGraduate College
thesis.degree.disciplineStatistics
thesis.degree.namePh.D.
refterms.dateFOA2023-09-14T08:39:12Z


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