Classification in high dimensional data using sparse techniques

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dc.contributor.advisorLamont, M. M. C.en_ZA
dc.contributor.authorStulumani, Agrippaen_ZA
dc.contributor.otherStellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.en_ZA
dc.date.accessioned2019-01-30T08:56:49Z
dc.date.accessioned2019-04-17T08:13:04Z
dc.date.available2019-01-30T08:56:49Z
dc.date.available2019-04-17T08:13:04Z
dc.date.issued2019-04
dc.descriptionThesis (MCom)--Stellenbosch University, 2019.en_ZA
dc.description.abstractENGLISH 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.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING : Geen opsomming beskikbaar.af_ZA
dc.format.extentviii, 84 pages ; illustrations, includes annexures
dc.identifier.urihttp://hdl.handle.net/10019.1/105792
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch University
dc.rights.holderStellenbosch University
dc.subjectHigh dimensional dataen_ZA
dc.subjectMathematical statisticsen_ZA
dc.subjectSparse classificationen_ZA
dc.subjectSparse gridsen_ZA
dc.subjectDimension reduction (Statistics)en_ZA
dc.subjectUCTD
dc.titleClassification in high dimensional data using sparse techniquesen_ZA
dc.typeThesisen_ZA
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