  Kendall transformation is a conversion of an ordered feature into a vector of
pairwise order relations between individual values. This way, it preserves
ranking of observations and represents it in a categorical form. Such
transformation allows for generalisation of methods requiring strictly
categorical input, especially in the limit of small number of observations,
when discretisation becomes problematic. In particular, many approaches of
information theory can be directly applied to Kendall-transformed continuous
data without relying on differential entropy or any additional parameters.
Moreover, by filtering information to this contained in ranking, Kendall
transformation leads to a better robustness at a reasonable cost of dropping
sophisticated interactions which are anyhow unlikely to be correctly estimated.
In bivariate analysis, Kendall transformation can be related to popular
non-parametric methods, showing the soundness of the approach. The paper also
demonstrates its efficiency in multivariate problems, as well as provides an
example analysis of a real-world data.
