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

要旨

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.

著者
Hyeon Jeon
Seoul National University, Seoul, Korea, Republic of
Hyunwook Lee
Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of
Yun-Hsin Kuo
University of California, Davis, Davis, California, United States
Taehyun Yang
Seoul National University, Seoul, Korea, Republic of
Daniel Archambault
Newcastle University, Newcastle, United Kingdom
Sungahn Ko
UNIST, Ulsan, Korea, Republic of
Takanori Fujiwara
Linköping University, Norrköping, Sweden
Kwan-Liu Ma
University of California at Davis, Davis, California, United States
Jinwook Seo
Seoul National University, Seoul, Korea, Republic of
DOI

10.1145/3706598.3713551

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713551

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Engaging with Data

Annex Hall F206
7 件の発表
2025-04-30 01:20:00
2025-04-30 02:50:00
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