  This paper proposes a new factor rotation for the context of functional
principal components analysis. This rotation seeks to re-represent a functional
subspace in terms of directions of decreasing smoothness as represented by a
generalized smoothing metric. The rotation can be implemented simply and we
show on two examples that this rotation can improve the interpretability of the
leading components.
