Adaptable Radial Axes Plots for Improved Multivariate Data Visualization
Adaptable Radial Axes Plots for Improved Multivariate Data Visualization
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DOI：
10.1111/cgf.13196
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发表时间：
2017-06
影响因子：
2.5
通讯作者：
M. Rubio-Sánchez;Alberto Sánchez;D. Lehmann
中科院分区：
文献类型：
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作者：
M. Rubio-Sánchez;Alberto Sánchez;D. Lehmann
径向轴图是扩展散点图的多变量可视化技术，以便将高维数据表示为可观察显示上的点。众所周知的方法包括星星坐标或主成分双标图，它们将数据属性表示为定义轴的向量，并产生线性降维映射。在本文中，我们提出了一个混合的方法，桥梁之间的差距差距星星坐标和主成分biplots，我们命名为“自适应径向轴图”。它基于解决凸优化问题，其中用户可以：（a）交互式地更新轴向量，如在星星坐标中，同时产生映射，使得能够通过标记的轴最佳地估计属性值，类似于主成分双标图;（B）使用不同的范数以探索数据的附加非线性映射;以及（c）在优化问题中包括权重和约束，用于沿沿着一个轴对数据进行排序。其结果是一种灵活的技术，补充，扩展和增强当前的径向数据分析方法。
Radial axes plots are multivariate visualization techniques that extend scatterplots in order to represent high‐dimensional data as points on an observable display. Well‐known methods include star coordinates or principal component biplots, which represent data attributes as vectors that define axes, and produce linear dimensionality reduction mappings. In this paper we propose a hybrid approach that bridges the gap between star coordinates and principal component biplots, which we denominate “adaptable radial axes plots”. It is based on solving convex optimization problems where users can: (a) update the axis vectors interactively, as in star coordinates, while producing mappings that enable to estimate attribute values optimally through labeled axes, similarly to principal component biplots; (b) use different norms in order to explore additional nonlinear mappings of the data; and (c) include weights and constraints in the optimization problems for sorting the data along one axis. The result is a flexible technique that complements, extends, and enhances current radial methods for data analysis.