I am studying about the dimension reduction in which I am studying about principal component analysis while studying it I came across that each component is the linear combination of the features .The basis is called loadings and I came to know that the loadings need to be standardized before using .I want to know that why it is necessary to normalize the loadings before creating the component.
The principal components are supplied with normalized version of original predictors. This is because, the original predictors may have different scales. For example: Imagine a data set with variables’ measuring units as gallons, kilometers, light years etc. It is definite that the scale of variances in these variables will be large.
Performing PCA on un-normalized variables will lead to insanely large loadings for variables with high variance. In turn, this will lead to dependence of a principal component on the variable with high variance. This is undesirable.