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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| Format: | Conference or Workshop Item |
| Language: | English |
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AIP Publishing
2016
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| Online Access: | http://umpir.ump.edu.my/id/eprint/9062/ http://umpir.ump.edu.my/id/eprint/9062/1/Identifying%20Influential%20Variables%20in%20Complex%20System-%20Network%20Topology%20Versus%20Principal%20Component%20Analysis.pdf |
| _version_ | 1848818393092718592 |
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| author | Nur Syahidah, Yusoff Shamshuritawati, Sharif |
| author_facet | Nur Syahidah, Yusoff Shamshuritawati, Sharif |
| author_sort | Nur Syahidah, Yusoff |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T01:36:57Z |
| format | Conference or Workshop Item |
| id | ump-9062 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:36:57Z |
| publishDate | 2016 |
| publisher | AIP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 (Published) http://dx.doi.org/10.1063/1.4954628 DOI: 10.1063/1.4954628 |
| spellingShingle | Q Science (General) Nur Syahidah, Yusoff Shamshuritawati, Sharif Identifying Influential Variables in Complex System: Network Topology Versus Principal Component Analysis |
| title | 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_short | 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 |
| topic | Q Science (General) |
| url | http://umpir.ump.edu.my/id/eprint/9062/ http://umpir.ump.edu.my/id/eprint/9062/ http://umpir.ump.edu.my/id/eprint/9062/ http://umpir.ump.edu.my/id/eprint/9062/1/Identifying%20Influential%20Variables%20in%20Complex%20System-%20Network%20Topology%20Versus%20Principal%20Component%20Analysis.pdf |