In PCA,why is finding the eigenvectors and eigenvalues of the covariance matrix of the data equivalent of fitting principal-component lines to the variance of the data?
I have understood eigen vectors in terms of matrix transformation.But why do eigen vectors of the co-variance matrix of the data give us the lines which express maximum variance?
