It is worth noting that additive distances (distances in a tree) cannot be perfectly preserved onto a 2D space. However, this fact is not a drawback in the present case because we are less concerned by distances preservation than subclasses segmentation. Indeed, even if the distance mapping is not perfect, it still emphasizes the various levels of the classification thanks to DD-HDS that avoids both "false neighbourhoods" (when a small distance in the output space becomes a large distance in the data space) and "tears" (when a large distance in the output space becomes a small distance in the data space). 19 