You’ve heard of PCA. But what’s really happening underneath?
PCA finds the directions (vectors) where your data varies the most.
Those directions are eigenvectors of the covariance matrix and the eigenvalues tell you how much variance each captures.
You’re basically rotating your data to find its “natural axes.”
PCA isn’t compression — it’s discovering how your data wants to be seen.