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Classification in high dimensional data using sparse techniques
Thesis (MCom)--Stellenbosch University, 2019.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2019
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| _version_ | 1867613926299009024 |
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| access_status_str | Open Access |
| author | Stulumani, Agrippa |
| author2 | Lamont, M. M. C. |
| author_browse | Lamont, M. M. C. Stulumani, Agrippa |
| author_facet | Lamont, M. M. C. Stulumani, Agrippa |
| author_sort | Stulumani, Agrippa |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MCom)--Stellenbosch University, 2019. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/105792 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:43:54.041Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/105792 Classification in high dimensional data using sparse techniques Stulumani, Agrippa Lamont, M. M. C. Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science. High dimensional data Mathematical statistics Sparse classification Sparse grids Dimension reduction (Statistics) UCTD Thesis (MCom)--Stellenbosch University, 2019. ENGLISH SUMMARY : Traditional classification techniques fail in the analysis of high-dimensional data. In response, new classification techniques and accompanying theory have recently emerged. These techniques are natural extensions of linear discriminant analysis. The aim is to solve the statistical challenges that arise with high-dimensional data by utilising the sparse coding (Johnstone and Titterington, 2009). In this project, our focus is on the following techniques: penalized LDA-FL, penalized LDA-FL, sparse discriminant analysis, sparse mixture discriminant analysis and sparse partial least squares. We evaluated the performance of these techniques in simulation studies and on two microarray gene expression datasets by comparing the test error rates and the number of features selected. In the simulation studies, we found that performance vary depending on the simulation set-up and on the classification technique used. The two microarray gene expression datasets are considered for practical implementation of these techniques. The results from the microarray gene expression datasets showed that these classification techniques achieve satisfactory accuracy. AFRIKAANSE OPSOMMING : Geen opsomming beskikbaar. Masters 2019-01-30T08:56:49Z 2019-04-17T08:13:04Z 2019-01-30T08:56:49Z 2019-04-17T08:13:04Z 2019-04 Thesis http://hdl.handle.net/10019.1/105792 en_ZA Stellenbosch University viii, 84 pages ; illustrations, includes annexures application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | High dimensional data Mathematical statistics Sparse classification Sparse grids Dimension reduction (Statistics) UCTD Stulumani, Agrippa Classification in high dimensional data using sparse techniques |
| title | Classification in high dimensional data using sparse techniques |
| title_full | Classification in high dimensional data using sparse techniques |
| title_fullStr | Classification in high dimensional data using sparse techniques |
| title_full_unstemmed | Classification in high dimensional data using sparse techniques |
| title_short | Classification in high dimensional data using sparse techniques |
| title_sort | classification in high dimensional data using sparse techniques |
| topic | High dimensional data Mathematical statistics Sparse classification Sparse grids Dimension reduction (Statistics) UCTD |
| url | http://hdl.handle.net/10019.1/105792 |
| work_keys_str_mv | AT stulumaniagrippa classificationinhighdimensionaldatausingsparsetechniques |