Eigenvectors, Eigenvalue, Data Science, Machine Learning, Principal Component.
only a handful of the leading eigenvectors are needed to explain most of the variance. Also notice in the plots above that the two eigenvectors are perpendicular or orthogonal. These eigenvectors are always orthogonal given that the covariance matrix is symmetric. This means the new features