Understanding Bias in Perceiving Dimensionality Reduction Projections
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Abstract (arXiv)
Selecting the dimensionality reduction technique that faithfully represents the structure is essential for reliable visual communication and analytics. In reality, however, practitioners favor projections for other attractions, such as aesthetics and visual saliency, over the projection's structural faithfulness, a bias we define as visual interestingness. In this research, we conduct a user study that (1) verifies the existence of such bias and (2) explains why the bias exists. Our study suggests that visual interestingness biases practitioners' preferences when selecting projections for analysis, and this bias intensifies with color-encoded labels and shorter exposure time. Based on our findings, we discuss strategies to mitigate bias in perceiving and interpreting DR projections.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 38 references that are in the map. First posted on arXiv 28 Jul 2025.
Builds on
9 of 38 references.
References · 9
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
- 2021Revisiting Dimensionality Reduction Techniques for Visual Cluster Analysis: An Empirical Studycited by 27
- 2025Unveiling High-dimensional Backstage: A Survey for Reliable Visual Analytics with Dimensionality Reductioncited by 16
- 2023ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction Embeddingscited by 15
- 2023CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clusteringcited by 8
- 2025A Critical Analysis of the Usage of Dimensionality Reduction in Four Domainscited by 7
- 2025Measuring the Validity of Clustering Validation Datasetscited by 6
- 2016Learning Interpretability for Visualizations using Adapted Cox Models through a User Experimentcited by 3
- 2024Shape It Up: An Empirically Grounded Approach for Designing Shape Palettescited by 2
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Citing papers per year: 2025: 1.
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