Performance analysis: an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables
Principal Components Analysis (PCA) is a variable reduction technique helps to reduce a complex dataset to a lower dimensional subspace. This study is interested to investigate an approach for handling a problem occurred from considering a very large number of measured variables followed by a classi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Medwell Publishing
2016
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/21553/1/RJAS%2011%2011%202016%201422-1426.pdf |
| _version_ | 1825804581135712256 |
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| author | Hamid, Hashibah Zainon, Fatinah Tan, Pei Yong |
| author_facet | Hamid, Hashibah Zainon, Fatinah Tan, Pei Yong |
| author_sort | Hamid, Hashibah |
| collection | UUM |
| description | Principal Components Analysis (PCA) is a variable reduction technique helps to reduce a complex dataset to a lower dimensional subspace. This study is interested to investigate an approach for handling a problem occurred from considering a very large number of measured variables followed by a classification task. For such purpose, PCA has been used to extract and reduce of a very large number of variables that considered in the study. Then, a Linear Discriminant Analysis (LDA) which is commonly used for classification is constructed based on the reduced set of variables. The performance analysis of the constructed PCA+LDA was conducted and compared with the classical LDA Model using different size of sample (n) and different number of independent variables (p). The performance of PCA+LDA and classical LDA Model has been evaluated based on misclassification rate. The results demonstrated that PCA+LDA performed better than the classical LDA Model for small sample case. For large sample size case, PCA+LDA also performed better than the classical LDA especially when the measured independent variables is too large.The overall findings showed that the constructed PCA+LDA can be considered as a good approach for handling a very large number of measured variables and performing classification treatment. |
| first_indexed | 2024-07-04T06:17:53Z |
| format | Article |
| id | uum-21553 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:17:53Z |
| publishDate | 2016 |
| publisher | Medwell Publishing |
| record_format | eprints |
| spelling | uum-215532017-04-06T06:52:46Z https://repo.uum.edu.my/id/eprint/21553/ Performance analysis: an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables Hamid, Hashibah Zainon, Fatinah Tan, Pei Yong QA Mathematics Principal Components Analysis (PCA) is a variable reduction technique helps to reduce a complex dataset to a lower dimensional subspace. This study is interested to investigate an approach for handling a problem occurred from considering a very large number of measured variables followed by a classification task. For such purpose, PCA has been used to extract and reduce of a very large number of variables that considered in the study. Then, a Linear Discriminant Analysis (LDA) which is commonly used for classification is constructed based on the reduced set of variables. The performance analysis of the constructed PCA+LDA was conducted and compared with the classical LDA Model using different size of sample (n) and different number of independent variables (p). The performance of PCA+LDA and classical LDA Model has been evaluated based on misclassification rate. The results demonstrated that PCA+LDA performed better than the classical LDA Model for small sample case. For large sample size case, PCA+LDA also performed better than the classical LDA especially when the measured independent variables is too large.The overall findings showed that the constructed PCA+LDA can be considered as a good approach for handling a very large number of measured variables and performing classification treatment. Medwell Publishing 2016 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/21553/1/RJAS%2011%2011%202016%201422-1426.pdf Hamid, Hashibah and Zainon, Fatinah and Tan, Pei Yong (2016) Performance analysis: an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables. Research Journal of Applied Sciences, 11 (11). pp. 1422-1426. ISSN 1815-932X https://www.medwelljournals.com/abstract/?doi=rjasci.2016.1422.1426 |
| spellingShingle | QA Mathematics Hamid, Hashibah Zainon, Fatinah Tan, Pei Yong Performance analysis: an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables |
| title | Performance analysis: an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables |
| title_full | Performance analysis: an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables |
| title_fullStr | Performance analysis: an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables |
| title_full_unstemmed | Performance analysis: an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables |
| title_short | Performance analysis: an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables |
| title_sort | performance analysis an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables |
| topic | QA Mathematics |
| url | https://repo.uum.edu.my/id/eprint/21553/1/RJAS%2011%2011%202016%201422-1426.pdf |
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