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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...

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Main Authors: Hamid, Hashibah, Zainon, Fatinah, Tan, Pei Yong
Format: Article
Language:English
Published: Medwell Publishing 2016
Subjects:
QA Mathematics
Online Access:http://repo.uum.edu.my/21553/1/RJAS%2011%2011%202016%201422-1426.pdf
http://repo.uum.edu.my/21553/
https://www.medwelljournals.com/abstract/?doi=rjasci.2016.1422.1426
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spelling my.uum.repo.215532017-04-06T06:52:46Z http://repo.uum.edu.my/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 http://repo.uum.edu.my/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
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA Mathematics
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
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.
format Article
author Hamid, Hashibah
Zainon, Fatinah
Tan, Pei Yong
author_facet Hamid, Hashibah
Zainon, Fatinah
Tan, Pei Yong
author_sort Hamid, Hashibah
title 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_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_sort performance analysis: an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables
publisher Medwell Publishing
publishDate 2016
url http://repo.uum.edu.my/21553/1/RJAS%2011%2011%202016%201422-1426.pdf
http://repo.uum.edu.my/21553/
https://www.medwelljournals.com/abstract/?doi=rjasci.2016.1422.1426
_version_ 1644283270413680640
score 13.244367

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