The Kernelized Taylor Diagram
This provides a new visualization tool for data analysis, but it is incremental as it builds on the existing Taylor diagram framework.
The paper tackles the limitations of the Taylor diagram, such as its inability to capture non-linear relationships and sensitivity to outliers, by proposing a kernelized Taylor diagram that visualizes similarities between data populations with minimal distributional assumptions, relating maximum mean discrepancy and kernel mean embedding in a single diagram.
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.