Eigenvectors, Principal Component, Dimensionality Reduction, Data Science, Unsupervised Learning.
the distance identified by PCA between each of the projected points and the origin be d1,d2,d3,d4,d5,d6 now, these distances are squared so that the negative values do not cancel out. 4.PCA calculates the sum of squared distances of all the projected points to this random line. 5. It keeps rotating