I perform Principal Component Analysis using two variables that are standardized. This is done by applying a SVD on the correlation matrix of the concerned variates. However, the SVD gives me the same eigenvector (weights) irrespective of what the two variables are. It's always
[.70710678, .70710678]. I find this strange. Of course, the eigenvalues differ.
My question is: How to interpret this?
PS. I wanted to conduct a total least squares regression on two variables. My statistical programme does not provide TLS, but TLS luckily equals Principal Component Analysis, as far as I know. Hence my question. The question is not about TLS directly, but why I get the same eigenvectors irrespective of which variables I use (as long as they are exactly 2).