Show me the steps to solve PCA can be explained from two different perspectives. What are the two perspectives explained in class? $2.$ The first principal direction is the direction in which the projections of the data points have the largest variance in the input space. We use $1$ to represent the first$/$largest eigenvalue of the covariance matrix, $1$ to denote the corresponding principal vector$/$direction $(1$ has unit length i$.$e$.,$ L$2$ norm is $1),$ to represent the sample mean, and $$ to