https://stats.stackexchange.com/a/3374/92071 - In PCA, the components are actual orthogonal linear combinations that maximize the total variance. In FA, the factors are linear combinations that maximize the shared portion of the variance--underlying "latent constructs".
Now, I understand that eigenvalues represent the amount of variance captured by a particular dimension. In order to obtain these directions, wouldn't one be maximising the co-variance terms along with the variance ones, implicitly?
Varimax rotation (for FA) maximises only the co-variance terms irrespective of the total variance associated with the newly formed dimension. Is this an accurate difference between the two kinds of rotation?