Using temporo-spatial principal component analysis as tool to dissociate latent ERP components of episodic memory retrieval: Objectifying time-window selection for overlapping ERP components

2022 ◽  
Vol 157 ◽  
pp. 105833
Author(s):  
André Haese ◽  
Daniela Czernochowski
Keyword(s):  
Principal Component Analysis ◽  
Episodic Memory ◽  
Memory Retrieval ◽  
Time Window ◽  
Principal Component ◽  
Component Analysis ◽  
Spatial Principal Component Analysis ◽  
Selection For ◽  
Window Selection

Feature Selection for Classification using Principal Component Analysis and Information Gain

2021 ◽  
Vol 174 ◽  
pp. 114765 ◽  
Author(s):  
Erick Odhiambo Omuya ◽  
George Onyango Okeyo ◽  
Michael Waema Kimwele
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Information Gain ◽  
Principal Component ◽  
Component Analysis ◽  
Selection For

Exploring competitiveness and wellbeing in Italy by spatial principal component analysis

2021 ◽  
pp. 141-146
Author(s):  
Carlo Cusatelli ◽  
Massimiliano Giacalone ◽  
Eugenia Nissi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Well Being ◽  
Component Analysis ◽  
Measurement Model ◽  
Statistical Technique ◽  
Multivariate Statistical ◽  
Spatial Principal Component Analysis ◽  
Italian Provinces ◽  
Reflective Measurement

Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces.

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.

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