It transforms the original features into principal components, which are uncorrelated numerical features.
This is achieved by dumping the principal components which has less variability.
Five principal components, which accounted for about 65.59 % (Table 5) of the total variance, were extracted for varimax rotation.
The analysis showed the principal components which account for about 70% of the total variability of the survey.
Using this criterion, we select six principal components, which will be used for comparison with the subsequent analysis.
PCA is designed to transform the original variables into new, uncorrelated variables (axes), called the principal components, which are linear combinations of the original variables.
We retained the 12 first principal components, which explain about 94% of the variance of the lower part of the face.
The reduction in the number of variables is obtained by transforming the original variables to a new smaller set of variables, called principal components, which were uncorrelated [38].
Subsequently, the DistLM modelling was conducted on the first three principal components, which accounted for almost all of the total variation, and the Bayesian selection criterion BIC was used as the 'stopping rule'.
PCA using Varimax with Kaiser normalization has resulted in the extraction of three main principal components which identifies the factors influencing each principal components for the physico-chemical parameters.
PCA is a technique, which converts the original variables into new uncorrelated variables (axes), known as principal components, which are linear combinations of the original variables (Sarbu and Pop 2005).
