ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2507.20805
108
1

Understanding Bias in Perceiving Dimensionality Reduction Projections

28 July 2025
Seoyoung Doh
Hyeon Jeon
Sungbok Shin
Ghulam Jilani Quadri
Nam Wook Kim
Jinwook Seo
    FAtt
ArXiv (abs)PDFHTML
Main:4 Pages
4 Figures
Bibliography:2 Pages
3 Tables
Abstract

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.

View on arXiv
Comments on this paper