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.
翻译：本文介绍了内核化的泰勒图,这是使数据人口相似化的图形框架;内核化的泰勒图以广泛使用的泰勒图为基础,而泰勒图是用来使人口相似化的图像;然而,泰勒图有若干局限性,例如没有捕捉非线性关系和对外部线的敏感度;为解决这些局限性,我们提议采用内核化的泰勒图;我们提议的内核化的泰勒图能够将人口之间的相似性与对数据分布的微小假设进行直观化。内核化的泰勒图涉及最大的平均差异,内核内核意味着嵌入一个单一的图中。 据我们所知,在这项工作之前没有设计出这种结构。我们认为,内核化的泰勒图可以成为数据可视化的宝贵工具。
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