While estimating L by the Maximum Likelihood method, Factor
Analysis identifies a rotation and a scale of L that contracts as
many coefficients to zero as possible. Having a sparse matrix L
increases the interpretability of the factor model, since any factor
will be created using only a few observable variables, hence
allowing us to bundle several inter-related variables and label
them as a factor.