提出新指标衡量降维图中的视觉失真，避免误导性分析。
Mind the Gaps: Measuring Visual Artifacts in Dimensionality Reduction
- 基于空区域保留度定义新质量指标，关注视觉保真度。
- 可检测传统指标忽略的投影中异常点与伪影。
- 适合数据可视化研究者和需要可信降维结果的用户。
降维（DR）技术常用于高维数据的二维可视化，但降维过程会引入扭曲，且不易察觉，可能导致错误结论。现有投影质量指标多关注全局或局部结构保持，却忽视了可视化本身的质量，常忽略异常点或视觉伪影。本文提出「形变指数」（Warping Index, WI），基于空区域保留对二维投影质量进行量化评估，认为正确保留点间空白区域对真实视觉呈现至关重要。
原文摘要 · Abstract (English)
Dimensionality Reduction (DR) techniques are commonly used for the visual exploration and analysis of high-dimensional data due to their ability to project datasets of high-dimensional points onto the 2D plane. However, projecting datasets in lower dimensions often entails some distortion, which is not necessarily easy to recognize but can lead users to misleading conclusions. Several Projection Quality Metrics (PQMs) have been developed as tools to quantify the goodness-of-fit of a DR projection; however, they mostly focus on measuring how well the projection captures the global or local structure of the data, without taking into account the visual distortion of the resulting plots, thus often ignoring the presence of outliers or artifacts that can mislead a visual analysis of the projection. In this work, we introduce the Warping Index (WI), a new metric for measuring the quality of DR projections onto the 2D plane, based on the assumption that the correct preservation of empty regions between points is of crucial importance towards a faithful visual representation of the data.
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