Document Type

Thesis - Open Access

Award Date

2018

Degree Name

Master of Science (MS)

Department

Mathematics and Statistics

First Advisor

Semhar Michael

Keywords

Custering, directional distributions, expectation maximization, mixtures, variable selection, von Mises-Fisher

Abstract

Mixtures of von Mises-Fisher distributions have been shown to be an effective model for clustering data on a unit hypersphere, but variable selection for these models remains an important and challenging problem. In this paper, we derive two variants of the expectation-maximization framework, which are each used to identify a specific type of irrelevant variables for these models. The first type are noise variables, which are not useful for separating any pairs of clusters. The second type are redundant variables, which may be useful for separating pairs of clusters, but do not enable any additional separation beyond the separability provided by some other variables. Removing these irrelevant variables is shown to improve cluster quality in simulated as well as benchmark datasets.

Description

Includes bibliographical references

Format

application/pdf

Number of Pages

41

Publisher

South Dakota State University

Rights

In Copyright - Non-Commercial Use Permitted
http://rightsstatements.org/vocab/InC-NC/1.0/

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