Exact methods for variable selection in principal component analysis: Guide functions and pre-selection

2013 ◽  
Vol 57 (1) ◽  
pp. 95-111 ◽  
Author(s):  
Joaquín Pacheco ◽  
Silvia Casado ◽  
Santiago Porras
Keyword(s):  
Principal Component Analysis ◽  
Variable Selection ◽  
Principal Component ◽  
Component Analysis ◽  
Exact Methods

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.

Sign in / Sign up

Export Citation Format

Share Document