  Principal loading analysis is a dimension reduction method that discards
variables which have only a small distorting effect on the covariance matrix.
We complement principal loading analysis and propose to rather use a mix of
both, the correlation and covariance matrix instead. Further, we suggest to use
rescaled eigenvectors and provide updated algorithms for all proposed changes.
