When do we use one or the other?
My use case:
I want to evaluate a linear space to see how good retrieval results are. I have a set of data X (m x n) and some weights W (m x 1). I want to measure the nearest neighbour retrieval performance on W'X with a ground truth value Y. This is a continuous value, so I can't use simple precision/recall.
If I use rank correlation, I will find the correlation between retrieved Ys and retrieval rank. If I use nDCG, I will use sorted Y to compute IDCG.
I would like to compare this to the correlation value I get when I change Y also. (For example, Y could be head pose angle in one case and age in another)