A Beginner’s Guide to Eigenvectors, Covariance, PCA and Entropy
I understand the concept of eigenvectors/eigenvalues, and I understand the concept of the covariance matrix, but I'm having a hard time understanding why the eigenvectors of the covariance matrix represent the principal components. Does anyone have a intuitive explanation they would like to share? Maybe I don't understand eigenvectors as well as I thought I did...