  We propose a non-parametric statistical procedure for detecting multiple
change-points in multidimensional signals. The method is based on a test
statistic that generalizes the well-known Kruskal-Wallis procedure to the
multivariate setting. The proposed approach does not require any knowledge
about the distribution of the observations and is parameter-free. It is
computationally efficient thanks to the use of dynamic programming and can also
be applied when the number of change-points is unknown. The method is shown
through simulations to be more robust than alternatives, particularly when
faced with atypical distributions (e.g., with outliers), high noise levels
and/or high-dimensional data.
