Three-dimensional Radial Visualization of High-dimensional Continuous or Discrete Data

Date
2019-01-01
Authors
Dai, Fan
Zhu, Yifan
Maitra, Ranjan
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Altmetrics
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Statistics
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Statistics
Abstract

This paper develops methodology for 3D radial visualization of high-dimensional datasets. Our display engine is called RadViz3D and extends the classic RadViz that visualizes multivariate data in the 2D plane by mapping every record to a point inside the unit circle. The classic RadViz display has equally-spaced anchor points on the unit circle, with each of them associated with an attribute or feature of the dataset. RadViz3D obtains equi-spaced anchor points exactly for the five Platonic solids and approximately for the other cases via a Fibonacci grid. We show that distributing anchor points at least approximately uniformly on the 3D unit sphere provides a better visualization than in 2D. We also propose a Max-Ratio Projection (MRP) method that utilizes the group information in high dimensions to provide distinctive lower-dimensional projections that are then displayed using Radviz3D. Our methodology is extended to datasets with discrete and mixed features where a generalized distributional transform is used in conjuction with copula models before applying MRP and RadViz3D visualization.

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This is a pre-print of the article Dai, Fan, Yifan Zhu, and Ranjan Maitra. "Three-dimensional Radial Visualization of High-dimensional Continuous or Discrete Data." arXiv preprint arXiv:1904.06366 (2019). Posted with permission.

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