Sanity check for class-coloring-based evaluation of dimension reduction techniques

Dimension Reduction techniques used to visualize multidimensional data provide a scatterplot spatialization of data similarities. A widespread way to evaluate the quality of such DR techniques is to use labeled data as a ground truth and to call the reader as a witness to qualify the visualization by looking at class-cluster correlations within the scatterplot. We expose the pitfalls of this evaluation process and we propose a principled solution to guide researchers to improve the way they use this visual evaluation of DR techniques.