You saw that it's typically important to transform the input variables to PCA so that they have zero mean and unit variance (this is called standardizing the variables). Why is standardization needed? Select all TRUE statements that apply. 1 point If some variables have a large variance and others have small variance, since PCA seeks to maximize overall variance, if you do *not* standardize the inputs, the directions of the principal components found by PCA will be heavily weighted towards the variable(s) with the largest variances. We usually want the results of PCA to be independent of the units of measurement of the variables. Transforming the data by column-centering ( subtracting the mean of each column from all the entries in that column, so that it has zero mean) allows us to perform PCA directly by doing Singular Value Decomposition