为多指标平行坐标图设计可解释的权重优化方法，让用户直观选择偏好。
Preference-Optimal Multi-Metric Weighting for Parallel Coordinate Plots
- 基于用户偏好的最优权重公式，自动计算多指标组合下的梯度颜色。
- 用UMAP降维后雷达图可视化指标权衡，支持多指标偏好选择。
- 在行人流引导规划中验证，不同偏好对应不同的参数重要性模式。
平行坐标图（PCPs）是解读控制参数与指标关系的常用方法，通常通过单一指标的颜色渐变来呈现。然而在多个指标并存时，如何实现有效渐变仍具挑战。现有简单线性加权法缺乏可解释性。为此，本文提出一种基于特定偏好指标组合的权重优化原则。对于双指标问题，用户可在二维平面上直接选择偏好；但多指标场景下需直观可视化以辅助决策。我们采用多种雷达图，在经UMAP降维后的二维平面中展示指标间的权衡关系。在行人流引导规划分析中，该方法揭示了不同用户偏好下控制参数的重要程度差异，验证了其有效性。
原文摘要 · Abstract (English)
Parallel coordinate plots (PCPs) are a prevalent method to interpret the relationship between the control parameters and metrics. PCPs deliver such an interpretation by color gradation based on a single metric. However, it is challenging to provide such a gradation when multiple metrics are present. Although a naive approach involves calculating a single metric by linearly weighting each metric, such weighting is unclear for users. To address this problem, we first propose a principled formulation for calculating the optimal weight based on a specific preferred metric combination. Although users can simply select their preference from a two-dimensional (2D) plane for bi-metric problems, multi-metric problems require intuitive visualization to allow them to select their preference. We achieved this using various radar charts to visualize the metric trade-offs on the 2D plane reduced by UMAP. In the analysis using pedestrian flow guidance planning, our method identified unique patterns of control parameter importance for each user preference, highlighting the effectiveness of our method.
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