A VARIATIONAL APPROACH TO CLUSTERING WITH LIPSCHITZ DECISION FUNCTIONS

Simple item page
dc.contributor.advisorSlud, Ericen_US
dc.contributor.authorZhou, Xiaoyuen_US
dc.contributor.departmentMathematical Statisticsen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2024-02-10T06:33:32Z
dc.date.available2024-02-10T06:33:32Z
dc.date.issued2023en_US
dc.description.abstractThis dissertation proposes an objective function based clustering approach using Lipschitzfunctions to represent the clustering function. We establish some mathematical properties including two optimality conditions and a uniqueness result; some statistical properties including two consistency results; and some computational development. This work is a step forward building upon existing work about Lipschitz classifiers to proceed from classification to clustering, also covering more theoretical and computational aspects. The mathematical contents strongly suggest further future analysis of the method. The general objective function might be of independent interest.en_US
dc.identifierhttps://doi.org/10.13016/dspace/ol4m-305c
dc.identifier.urihttp://hdl.handle.net/1903/31667
dc.language.isoenen_US
dc.subject.pqcontrolledStatisticsen_US
dc.titleA VARIATIONAL APPROACH TO CLUSTERING WITH LIPSCHITZ DECISION FUNCTIONSen_US
dc.typeDissertationen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Zhou_umd_0117E_23792.pdf
Size:
1.64 MB
Format:
Adobe Portable Document Format
Download

Collections

UMD Theses and Dissertations
Mathematics Theses and Dissertations