Robust linear discriminant analysis with automatic trimmed mean
Linear discriminant analysis (LDA) is a multivariate statistical technique used to determine which continuous variables discriminate between two or more naturally occurring groups. This technique creates a linear discriminant function that yields optimal classification rule between two or more group...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Universiti Teknikal Malaysia Melaka
2016
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| Online Access: | https://repo.uum.edu.my/id/eprint/20520/1/JTEC%208%2010%202016%201%203.pdf |
| _version_ | 1825804385104429056 |
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| author | Syed Yahaya, Sharipah Soaad Lim, Yai-Fung Ali, Hazlina Omar, Zurni |
| author_facet | Syed Yahaya, Sharipah Soaad Lim, Yai-Fung Ali, Hazlina Omar, Zurni |
| author_sort | Syed Yahaya, Sharipah Soaad |
| collection | UUM |
| description | Linear discriminant analysis (LDA) is a multivariate statistical technique used to determine which continuous variables discriminate between two or more naturally occurring groups. This technique creates a linear discriminant function that yields optimal classification rule between two or more groups under the assumptions of normality and homoscedasticity.Nonetheless, the computation of parametric LDA which are based on the sample mean vectors and pooled sample covariance matrix are known to be sensitive to nonnormality.To overcome the sensitivity of this method towards non-normality as well as homoscedasticity, this study proposed a new robust LDA method.Through this approach, an automatic trimmed mean vector was used as a substitute for the usual mean vector in the parametric LDA. Meanwhile, for the covariance matrix, this study introduced a robust approach by multiplying the Spearman’s rho with the corresponding robust scale estimator used in the trimming process. Simulated and real financial data were used to test the performance of the proposed method in terms of misclassification rate.The results showed that the new method performed better compared to the parametric LDA and the existing robust LDA with S-estimator |
| first_indexed | 2024-07-04T06:13:40Z |
| format | Article |
| id | uum-20520 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:13:40Z |
| publishDate | 2016 |
| publisher | Universiti Teknikal Malaysia Melaka |
| record_format | eprints |
| spelling | uum-205202017-01-03T09:01:58Z https://repo.uum.edu.my/id/eprint/20520/ Robust linear discriminant analysis with automatic trimmed mean Syed Yahaya, Sharipah Soaad Lim, Yai-Fung Ali, Hazlina Omar, Zurni QA75 Electronic computers. Computer science Linear discriminant analysis (LDA) is a multivariate statistical technique used to determine which continuous variables discriminate between two or more naturally occurring groups. This technique creates a linear discriminant function that yields optimal classification rule between two or more groups under the assumptions of normality and homoscedasticity.Nonetheless, the computation of parametric LDA which are based on the sample mean vectors and pooled sample covariance matrix are known to be sensitive to nonnormality.To overcome the sensitivity of this method towards non-normality as well as homoscedasticity, this study proposed a new robust LDA method.Through this approach, an automatic trimmed mean vector was used as a substitute for the usual mean vector in the parametric LDA. Meanwhile, for the covariance matrix, this study introduced a robust approach by multiplying the Spearman’s rho with the corresponding robust scale estimator used in the trimming process. Simulated and real financial data were used to test the performance of the proposed method in terms of misclassification rate.The results showed that the new method performed better compared to the parametric LDA and the existing robust LDA with S-estimator Universiti Teknikal Malaysia Melaka 2016 Article PeerReviewed application/pdf en cc_by https://repo.uum.edu.my/id/eprint/20520/1/JTEC%208%2010%202016%201%203.pdf Syed Yahaya, Sharipah Soaad and Lim, Yai-Fung and Ali, Hazlina and Omar, Zurni (2016) Robust linear discriminant analysis with automatic trimmed mean. Journal of Telecommunication, Electronic and Computer Engineering, 8 (10). pp. 1-3. ISSN 2180-1843 http://journal.utem.edu.my/index.php/jtec/article/view/1356 |
| spellingShingle | QA75 Electronic computers. Computer science Syed Yahaya, Sharipah Soaad Lim, Yai-Fung Ali, Hazlina Omar, Zurni Robust linear discriminant analysis with automatic trimmed mean |
| title | Robust linear discriminant analysis with automatic trimmed mean |
| title_full | Robust linear discriminant analysis with automatic trimmed mean |
| title_fullStr | Robust linear discriminant analysis with automatic trimmed mean |
| title_full_unstemmed | Robust linear discriminant analysis with automatic trimmed mean |
| title_short | Robust linear discriminant analysis with automatic trimmed mean |
| title_sort | robust linear discriminant analysis with automatic trimmed mean |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/20520/1/JTEC%208%2010%202016%201%203.pdf |
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