mlsanityData to 14 Oct 2026

Unveiling High-dimensional Backstage: A Survey for Reliable Visual Analytics with Dimensionality Reduction

arXiv 2501.10168 ↗First posted 17 Jan 2025cs.HCcited by 16

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Abstract (arXiv)

Dimensionality reduction (DR) techniques are essential for visually analyzing high-dimensional data. However, visual analytics using DR often face unreliability, stemming from factors such as inherent distortions in DR projections. This unreliability can lead to analytic insights that misrepresent the underlying data, potentially resulting in misguided decisions. To tackle these reliability challenges, we review 133 papers that address the unreliability of visual analytics using DR. Through this review, we contribute (1) a workflow model that describes the interaction between analysts and machines in visual analytics using DR, and (2) a taxonomy that identifies where and why reliability issues arise within the workflow, along with existing solutions for addressing them. Our review reveals ongoing challenges in the field, whose significance and urgency are validated by five expert researchers. This review also finds that the current research landscape is skewed toward developing new DR techniques rather than their interpretation or evaluation, where we discuss how the HCI community can contribute to broadening this focus.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jan 2025.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 8; 2026: 8.

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  1. 2025Stop Misusing t-SNE and UMAP for Visual Analyticscited by 8
  2. 2025Measuring the Validity of Clustering Validation Datasetscited by 6
  3. 2025Metric Design != Metric Behavior: Improving Metric Selection for the Unbiased Evaluation of Dimensionality Reductioncited by 2
  4. 2026Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Frameworkcited by 2
  5. 2025Dataset-Adaptive Dimensionality Reductioncited by 1
  6. 2025Visual Analytics Using Tensor Unified Linear Comparative Analysiscited by 1
  7. 2025Understanding Bias in Perceiving Dimensionality Reduction Projectionscited by 1
  8. 2025How Scale Breaks "Normalized Stress" and KL Divergence: Rethinking Quality Metricscited by 1
  9. 2026MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysiscited by 1
  10. 2025Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reductioncited by 0
  11. 2026IPBC: An Interactive Projection-Based Framework for Human-in-the-Loop Semi-Supervised Clustering of High-Dimensional Datacited by 0
  12. 2026A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reductioncited by 0
4 more
  1. 2026Class Angular Distortion Index for Dimensionality Reductioncited by 0
  2. 2026Topo-GS: Continuous Volumetric Embedding of High-Dimensional Data via Topological Gaussian Splattingcited by 0
  3. 2026FADEx: Feature Attribution and Distortion-based Explanation of Dimensionality Reductioncited by 0
  4. 2026Understanding Differentiable Embeddings Through Differential and Integral Geometrycited by 0