Principal Component, Machine Learning, Artificial Intelligence, Linear Algebra, Data Science.
that we could not achieve any more extra variance by shifting the line a bit up or down. Of course, when we convert the “x1 axis” vector in our eigenbasis world back to the cartesian plane, we remember that it is just the eigenvector x1. So, x1 is the line that maximizes the variance. Going back to