Maximal Autocorrelation Functions in Functional Data Analysis
  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. 
Giles Hooker and Steven Roberts
stat.ME