I was reading this Reddit thread on principal component analysis (PCA). One user responded with the following:
There is nothing that would suggest that PC's are meaningful. Quite the opposite, because they are forced to be orthogonal to each other you pretty much guarantee that they will NOT be meaningful at all.
Unless the phenomenon you're modeling also happens to have orthogonal features, such as axis of rotation so pitch, yaw, roll that are orthogonal to each other, but this pretty much never happens.
Trying to interpret principal components is like the classical newbie mistake. People do it because some ancient statistical software implemented PCA as a special case of factor analysis and they confuse FA with PCA and mix/match them and really mean FA when they say PCA in their papers.
Is it true that PCA assumes that your features are orthogonal? Does this mean that PCA is not a good technique when features are not orthogonal?
EDIT: Given the (now deleted) comment, I want to say that I have no idea whether this is referring to the original features or the latent features. Furthermore, since I'm a novice to this, I don't even have a good idea of what the difference between these would be. If your answer addresses this, I would appreciate an explanation of the differences between original and latent features in this context.