警告：t-SNE和UMAP常误用于分析聚类间关系，因使用者缺乏降维常识。
Stop Misusing t-SNE and UMAP for Visual Analytics
- 通过调研136篇论文与专家访谈，发现滥用源于从业者降维知识不足。
- 现有学术论文对纠正滥用效果甚微，因未能触及实际使用场景。
- 适合数据可视化初学者、研究者及审稿人警惕降维结果的误导性。
t-SNE和UMAP在可视化分析中的误用日益普遍。尽管其投影常无法忠实反映原始聚类间距离，但研究人员仍频繁用其探究聚类关系。本文通过调研136篇相关论文，访谈使用降维技术的研究人员及降维领域专家，揭示误用根源主要在于从业者降维素养不足。同时指出，以往基于学术论文的纠正尝试收效甚微。基于这些发现，本文探讨未来研究方向，以缓解该问题。
原文摘要 · Abstract (English)
Misuses of t-SNE and UMAP in visual analytics have become increasingly common. For example, although t-SNE and UMAP projections often do not faithfully reflect the original distances between clusters, practitioners frequently use them to investigate inter-cluster relationships. We investigate why this misuse occurs, and discuss methods to prevent it. To that end, we first review 136 papers to verify the prevalence of the misuse. We then interview researchers who have used dimensionality reduction (DR) to understand why such misuse occurs. Finally, we interview DR experts to examine why previous efforts failed to address the misuse. We find that the misuse of t-SNE and UMAP stems primarily from limited DR literacy among practitioners, and that existing attempts to address this issue -- mostly based on academic papers -- have been ineffective. Based on these insights, we discuss potential future research directions to mitigate the misuse.
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