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http://hdl.handle.net/11375/29024
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DC Field | Value | Language |
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dc.contributor.advisor | McNicholas, Paul D. | - |
dc.contributor.author | Flynn, Thomas J. | - |
dc.date.accessioned | 2023-10-11T15:57:40Z | - |
dc.date.available | 2023-10-11T15:57:40Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://hdl.handle.net/11375/29024 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.subject | Evolutionary Algorithm;Model-based Clustering;EM Algorithm;Matrix-Variate | en_US |
dc.title | An Evolutionary Algorithm for Matrix-Variate Model-Based Clustering | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Mathematics and Statistics | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Science (MSc) | en_US |
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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