I constructed a PCA plot from a very high-dimensional dataset that contains features relating to fraud. After creating the PCA plot, I created a biplot with the features to see how they interact. The biplot is shown below:
I'm slightly confused with interpreting the 4 separate quadrants.
In the bottom left quadrant, a small number of features have negative scores for both PCA 1 and PCA 2 and I have therefore assumed they explain little variance for both PCs although these features are assumingly correlate given they are clustered?
In the bottom right quadrant, we see again that a number of features are clustered but this time posses positive scores for PCA 1 - should this be interpreted as these features having a lot of influence on the variation captured by PCA 1? and vice-versa for the top left quadrant.
The top right quadrant has the most features and these features score positively for both PCA 1 and PCA 2 which I've interpreted as these features being the most important in my dataset as influence a lot of variation in both PCs.
I've also concluded that PCA 1 accounts for the most variants and the features it has in its positive quadrants should be focused on.
Is this a fair assessment and interpretation of the Biplot that I have created? I'm not sure that I'm looking at the core tenets of a PCA biplot or that perhaps I'm missed something. Thank you very much in advance!
