The number of Eigenvectors selected after a PCA transformation can be done in several ways. One such method is using the total amount of variance explained by each principal component. That is, select the top $n$ Eigenvectors such that the cumulative variance explained by them reaches a $p$ (e.g., 99%).
This notion was applied in my paper. However, the reviewers require an analysis on how I came to this decision or a relevant citation perhaps.
I learnt this through Andrew Ng's Machine Learning course in Coursera, but I'm not able to find a solid published reference to this anywhere.
Can someone please provide an apt citation that states this concept or analyses it in any way?