Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/29024
Title: | An Evolutionary Algorithm for Matrix-Variate Model-Based Clustering |
Authors: | Flynn, Thomas J. |
Advisor: | McNicholas, Paul D. |
Department: | Mathematics and Statistics |
Keywords: | Evolutionary Algorithm;Model-based Clustering;EM Algorithm;Matrix-Variate |
Publication Date: | 2023 |
Abstract: | Model-based clustering is the use of finite mixture models to identify underlying group structures in data. Estimating parameters for mixture models is notoriously difficult, with the expectation-maximization (EM) algorithm being the predominant method. An alternative approach is the evolutionary algorithm (EA) which emulates natural selection on a population of candidate solutions. By leveraging a fitness function and genetic operators like crossover and mutation, EAs offer a distinct way to search the likelihood surface. EAs have been developed for model-based clustering in the multivariate setting; however, there is a growing interest in matrix-variate distributions for three-way data applications. In this context, we propose an EA for finite mixtures of matrix-variate distributions. |
URI: | http://hdl.handle.net/11375/29024 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Flynn_Thomas_J_finalsubmission202309_MSc.pdf | 306.33 kB | Adobe PDF | View/Open |
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