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Identifying Influential Variables in Complex System: Network Topology Versus Principal Component Analysis

High dimensional covariance structure can be considered as a complex system that relates each variable to the others in terms of variability. In complex system, identifying influential variables is a very important part of reliability analysis, which has been a key issue in analysing the structural...

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Main Authors: Nur Syahidah, Yusoff, Shamshuritawati, Sharif
Format: Conference or Workshop Item
Language:English
Published: AIP Publishing 2016
Subjects:
Q Science (General)
Online Access:http://umpir.ump.edu.my/id/eprint/9062/1/Identifying%20Influential%20Variables%20in%20Complex%20System-%20Network%20Topology%20Versus%20Principal%20Component%20Analysis.pdf
http://umpir.ump.edu.my/id/eprint/9062/
http://dx.doi.org/10.1063/1.4954628
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spelling my.ump.umpir.90622017-03-29T01:24:34Z http://umpir.ump.edu.my/id/eprint/9062/ Identifying Influential Variables in Complex System: Network Topology Versus Principal Component Analysis Nur Syahidah, Yusoff Shamshuritawati, Sharif Q Science (General) High dimensional covariance structure can be considered as a complex system that relates each variable to the others in terms of variability. In complex system, identifying influential variables is a very important part of reliability analysis, which has been a key issue in analysing the structural organization of a system. To analyse such complex system, network topology and principal component analysis are constructed to simplify the system. Network topology can be used to simplify the information about the system and centrality measure will be used to interpret the network. In the other hand, the principal component analysis can be used to eliminate the variables that contribute little extra information. An example will be discussed to illustrate the advantage and disadvantage of network topology and principal component analysis and a recommendation will be presented. AIP Publishing 2016 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/9062/1/Identifying%20Influential%20Variables%20in%20Complex%20System-%20Network%20Topology%20Versus%20Principal%20Component%20Analysis.pdf Nur Syahidah, Yusoff and Shamshuritawati, Sharif (2016) Identifying Influential Variables in Complex System: Network Topology Versus Principal Component Analysis. In: AIP Conference Proceedings: Advances in Industrial and Applied Mathematics Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences (SKSM23), 24–26 November 2015 , Johor Bahru, Malaysia. pp. 1-6., 1750 (060023). ISBN 978-0-7354-1407-5 http://dx.doi.org/10.1063/1.4954628 DOI: 10.1063/1.4954628
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Nur Syahidah, Yusoff
Shamshuritawati, Sharif
Identifying Influential Variables in Complex System: Network Topology Versus Principal Component Analysis
description High dimensional covariance structure can be considered as a complex system that relates each variable to the others in terms of variability. In complex system, identifying influential variables is a very important part of reliability analysis, which has been a key issue in analysing the structural organization of a system. To analyse such complex system, network topology and principal component analysis are constructed to simplify the system. Network topology can be used to simplify the information about the system and centrality measure will be used to interpret the network. In the other hand, the principal component analysis can be used to eliminate the variables that contribute little extra information. An example will be discussed to illustrate the advantage and disadvantage of network topology and principal component analysis and a recommendation will be presented.
format Conference or Workshop Item
author Nur Syahidah, Yusoff
Shamshuritawati, Sharif
author_facet Nur Syahidah, Yusoff
Shamshuritawati, Sharif
author_sort Nur Syahidah, Yusoff
title Identifying Influential Variables in Complex System: Network Topology Versus Principal Component Analysis
title_short Identifying Influential Variables in Complex System: Network Topology Versus Principal Component Analysis
title_full Identifying Influential Variables in Complex System: Network Topology Versus Principal Component Analysis
title_fullStr Identifying Influential Variables in Complex System: Network Topology Versus Principal Component Analysis
title_full_unstemmed Identifying Influential Variables in Complex System: Network Topology Versus Principal Component Analysis
title_sort identifying influential variables in complex system: network topology versus principal component analysis
publisher AIP Publishing
publishDate 2016
url http://umpir.ump.edu.my/id/eprint/9062/1/Identifying%20Influential%20Variables%20in%20Complex%20System-%20Network%20Topology%20Versus%20Principal%20Component%20Analysis.pdf
http://umpir.ump.edu.my/id/eprint/9062/
http://dx.doi.org/10.1063/1.4954628
_version_ 1643666027894013952
score 13.211869

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