提出环形坐标系，实现高维数据无损可视化与交互
High-Dimensional Data Classification in Concentric Coordinates
- 用环形坐标系替代传统平行坐标，压缩高维数据展示空间
- 支持无损可视化且避免重叠遮挡，计算效率更高
- 适合需要可解释性的人机协作机器学习场景
高维数据的可解释可视化仍受限于现有方法在无损显示高维数据时易产生重叠遮挡，且难以高效参数化。本文提出一种支持低维到高维数据的环形坐标框架，该方法是平行坐标和圆形坐标的紧凑推广，属于广义线坐标可视化形式，可直接支持机器学习算法的可视化并促进人机交互。
原文摘要 · Abstract (English)
The visualization of multi-dimensional data with interpretable methods remains limited by capabilities for both high-dimensional lossless visualizations that do not suffer from occlusion and that are computationally capable by parameterized visualization. This paper proposes a low to high dimensional data supporting framework using lossless Concentric Coordinates that are a more compact generalization of Parallel Coordinates along with former Circular Coordinates. These are forms of the General Line Coordinate visualizations that can directly support machine learning algorithm visualization and facilitate human interaction.
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