论文标题
内核泰勒图
The Kernelized Taylor Diagram
论文作者
论文摘要
本文介绍了泰勒图，这是一个图形框架，用于可视化数据人群之间的相似性。泰勒图的内核图建立在广泛使用的泰勒图上，该图用于可视化人群之间的相似性。但是，泰勒图有几个局限性，例如不捕获非线性关系和对异常值的敏感性。为了解决此类局限性，我们提出了泰勒图。我们提出的内核泰勒图能够可视化数据分布的假设最少的人群之间的相似性。内核图泰勒图将最大平均差异与单个图中的内核平均值相关联，据我们所知，这种结构尚未在这项工作之前设计。我们认为，泰勒图的内核图可以是数据可视化的宝贵工具。
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.
