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Penalized feature selection in model-based clustering
Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2023
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| _version_ | 1867613531366490112 |
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| access_status_str | Open Access |
| author2 | Millard, Sollie M. |
| author_browse | Millard, Sollie M. |
| author_facet | Millard, Sollie M. |
| collection | Thesis |
| dc_rights_str_mv | © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/91035 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:37.672Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/91035 Penalized feature selection in model-based clustering Millard, Sollie M. luan3potgieter@gmail.com Kanfer, F.H.J. (Frans) Potgieter, Luandrie UCTD Variable selection Clustering Expectation Maximisation Penalized log-likelihood Penalized feature selection Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2022. Cluster analysis is a popular unsupervised statistical method used to group observations into clusters. Identifying latent segments and groupings in the data aids in the understanding of natural phenomena. The data driven society we live in today has made high dimensional data quite ubiquitous and hence noise variables are unavoidable. Modelbased clustering methods have had to adjust in order to identify these non-informative variables since they unduly increase a model’s complexity. This mini dissertation reviews the effectiveness of different penalized likelihood approaches and how they aid in identifying and removing uninformative variables. An EM algorithm is used to fit a penalized Gaussian mixture model to the data. The penalized log likelihood is maximized and if a variable’s parameter estimates are reduced to the same value across all clusters, it is removed from the model and deemed uninformative. It was found that by penalizing the mean, uninformative variables were successfully identified and removed. CSIR Statistics MSc (Advanced Data Analytics) Unrestricted 2023-06-06T13:00:21Z 2023-06-06T13:00:21Z 2023-09-01 2022 Mini Dissertation * S2023 http://hdl.handle.net/2263/91035 10.25403/UPresearchdata.23219531 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Variable selection Clustering Expectation Maximisation Penalized log-likelihood Penalized feature selection Penalized feature selection in model-based clustering |
| title | Penalized feature selection in model-based clustering |
| title_full | Penalized feature selection in model-based clustering |
| title_fullStr | Penalized feature selection in model-based clustering |
| title_full_unstemmed | Penalized feature selection in model-based clustering |
| title_short | Penalized feature selection in model-based clustering |
| title_sort | penalized feature selection in model based clustering |
| topic | UCTD Variable selection Clustering Expectation Maximisation Penalized log-likelihood Penalized feature selection |
| url | http://hdl.handle.net/2263/91035 |