  This paper presents the kernelized Taylor diagram, a graphical framework for
visualizing similarities between data populations. The kernelized Taylor
diagram builds on the widely used Taylor diagram, which is used to visualize
similarities between populations. However, the Taylor diagram has several
limitations such as not capturing non-linear relationships and sensitivity to
outliers. To address such limitations, we propose the kernelized Taylor
diagram. Our proposed kernelized Taylor diagram is capable of visualizing
similarities between populations with minimal assumptions of the data
distributions. The kernelized Taylor diagram relates the maximum mean
discrepancy and the kernel mean embedding in a single diagram, a construction
that, to the best of our knowledge, have not been devised prior to this work.
We believe that the kernelized Taylor diagram can be a valuable tool in data
visualization.
