aiqbench analysis
This paper addresses a critical literacy gap in machine learning practice by exposing how researchers frequently misinterpret t-SNE and UMAP visualizations as faithful representations of inter-cluster distances. Through empirical analysis of 136 papers and expert interviews, the authors identify limited dimensionality reduction knowledge among practitioners as the primary driver behind these errors rather than flaws in the algorithms themselves. The work offers a necessary corrective for data scientists aiming to ensure rigorous and accurate interpretation of high-dimensional model outputs.
Stop Misusing t-SNE and UMAP for Visual Analytics
arXiv:2506.08725v3 Announce Type: replace-cross Abstract: 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.
Abstract: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.
Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
arXiv:2506.08725 [cs.HC]
(or arXiv:2506.08725v3 [cs.HC] for this version)
Full text under the publisher's open licence · 158 words
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