The Kernelized Taylor Diagram

TL;DR

The paper introduces the kernelized Taylor diagram, a new visualization tool that captures non-linear relationships and reduces outlier sensitivity in comparing data populations, by integrating kernel methods with the traditional Taylor diagram.

Contribution

It proposes the kernelized Taylor diagram, combining maximum mean discrepancy and kernel mean embedding into a single visualization, addressing limitations of the original Taylor diagram.

Findings

Visualizes non-linear data relationships effectively

Reduces sensitivity to outliers in data comparison

Integrates kernel methods with traditional Taylor diagram

Abstract

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.