  We introduce a method for deterministic decoupling of global features and
show its applicability to improve data analysis performance, as well as to open
new venues for feature transfer. We propose a new formalism that is based on
defining transformations on submanifolds, by following trajectories along the
features gradients. Through these transformations we define a normalization
that, we demonstrate, allows for decoupling differentiable features. By
applying this to sampling moments, we obtain a quasi-analytic solution for the
orthokurtosis, a normalized version of the kurtosis that is not just decoupled
from mean and variance, but also from skewness. We apply this method in the
original data domain and at the output of a filter bank to regression and
classification problems based on global descriptors, obtaining a consistent and
significant improvement in performance as compared to using classical
(non-decoupled) descriptors.
