I have a matrix of let's say 120 variables and 50 subjects (rows). Before computing correlation between the 120 variables, I want to perform principal copmonent analysis (PCA) on this matrix. I will find 120 eigenvalues. Before sorting them and deciding which ones to keep, I have a problem in interpretation. **Do these eigenvalues correspond to the original variables (first eigenvalue -- first variable, etc.)?**

So if I reject let's say the 10th eigenvalue, does it mean that the 10th variable (among the 120) must be rejected?