Improved leaps and bounds variable selection algorithm based on principal component analysis

2014 ◽  
Vol 139 ◽  
pp. 76-83
Author(s):  
Wenjun Zhang ◽  
Xin Wang ◽  
Lin Chen
Keyword(s):  
Principal Component Analysis ◽  
Variable Selection ◽  
Principal Component ◽  
Component Analysis ◽  
Selection Algorithm

scholarly journals Process monitoring based on distributed principal component analysis with angle-relevant variable selection

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771985758 ◽  
Author(s):  
Chen Xu ◽  
Fei Liu
Keyword(s):  
Principal Component Analysis ◽  
Variable Selection ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Process Variables ◽  
Analysis Model ◽  
Relevant Variable ◽  
Selection For ◽  
Monitoring Performance

Multivariate statistics process monitoring can achieve dimensionality reduction and latent feature extraction on process variables. However, process variables without beneficial information may affect the monitoring performance. This article proposes a distributed principal component analysis method based on the angle-relevant variable selection for plant-wide process monitoring. The directions of principal components are utilized to construct the sub-blocks, where the variables in each sub-block are determined by angle. After establishing the principal component analysis model in each sub-block, the monitoring results are fused by Bayesian inference. The simulation results show that the proposed method can select the responsible variables effectively and enhance the monitoring performance.

scholarly journals Research on the order parameter selection algorithm based on correlation analysis and principal component analysis——Taking the Logistics sector in Gansu Province as an example

2020 ◽  
Vol 214 ◽  
pp. 02031
Author(s):  
Mu Dan ◽  
Shi Chuwei
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Social System ◽  
Order Parameter ◽  
Principal Component ◽  
Component Analysis ◽  
Parameter Selection ◽  
Order Parameters ◽  
Gansu Province ◽  
Selection Algorithm

An order parameter selection algorithm based on correlation analysis and principal component analysis was designed according to the statistical analysis method, the selection principle of order parameters of social system, and the correlation test in correlation analysis and the variable contribution test in principal component analysis in this paper. The redundant variables were eliminated from the system by correlation analysis first, and then the variables with high contribution to the system were selected by principal component analysis, so the order parameters obtained accordingly not only have low information redundancy, but also reflect the actual information of the social system to the greatest extent. At the end of this paper, the logistics sector in Gansu Province was taken as an example to select the panel data from 2006 to 2015. Eight indices were extracted as the order parameters of the logistics sector in Gansu Province from the sixteen indices which are redundant selected by this algorithm. The order parameters selected by rational judgment reflect 99% of the original information. The results show that the order parameters in the social system can be correctly and reasonably selected by this order parameter selection algorithm based on correlation analysis and principal component analysis.

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