I was reading a paper called "Production Optimization Using Machine Learning in Bakken Shale", and came across an approach I was a bit puzzled by. Unfortunately, the paper is behind a paywall, but I will explain the case:
The researchers have a dataset with 14 predictors, representing different geological properties, well-design properties, etc. In order to conduct feature selection, they first run a Singular Value Decomposition (SVD), and state that "eight principal components can explain more than 90% of total input variance":
Further they run a Random Forest (RF) with all 14 features, and rank the features according to their Variable Importance score. Additionally, they perform Recursive Feature Elimination (RFE) which also ranks the features.
After this, they refer to the output from the SVD and state "as eight input parameters would be enough, the features we select for our deep learning model are:", and then they list the 8 features that were ranked highest over the two abovementioned methods (RF and RFE).
My question is: Is this a valid way of utilizing the output from a Singular Value Decomposition? I figured these "eight principal components" would be some kind of transformed versions of the original variables, thus not making it directly valid to apply this insight to the variables in their original form. Please correct me if I'm wrong!