Visualizing multivariate data using singularity theory
Visualizing multivariate data using singularity theory
复制标题
使用奇点理论可视化多元数据
DOI：
10.1007/978-4-431-54907-9_4
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
and T. Yamamoto
中科院分区：
文献类型：
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作者：
O. Saeki;S. Takahashi;D. Sakurai;Hsiang-Yun Wu;K. Kikuchi;H. Carr;D. Duke;and T. Yamamoto
这是一篇关于大数据可视化的最新发展的调查文章，特别是多变量体数据。我们提出两个基本要素。首先是数学背景，特别是可微映射的奇点理论，它使我们能够以数学上严格的方式捕捉给定多元数据的拓扑特征。第二种是计算机科学的新发展，称为联合轮廓网，它可以以一种有效和鲁棒的方式对给定的多元数据集的拓扑结构进行编码。一些应用程序的真实的数据分析。
This is a survey article on recent developments in visualization of large data, especially that of multivariate volume data. We present two essential ingredients. The first one is the mathematical background, especially the singularity theory of differentiable mappings, which enables us to capture topological features of given multivariate data in a mathematically rigorous way. The second one is a new development in computer science, called the joint contour net, which can encode topological structures of a given set of multivariate data in an efficient and robust way. Some applications to real data analysis are also presented.