Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Visual analytics using dimensionality reduction (DR) can easily be unreliable for various reasons, e.g., inherent distortions in representing the original data. The literature has thus proposed a wide range of methodologies to make DR-based visual analytics reliable. However, the diversity and extensiveness of the literature can leave novice analysts and researchers uncertain about where to begin and proceed. To address this problem, we propose a guide for reading papers for reliable visual analytics with DR. Relying on the previous classification of the relevant literature, our guide helps both practitioners to (1) assess their current DR expertise and (2) identify papers that will further enhance their understanding. Interview studies with three experts in DR and data visualizations validate the significance, comprehensiveness, and usefulness of our guide.
Description
CCS Concepts: Human-centered computing → Visual analytics; Mathematics of computing → Dimensionality reduction
@inproceedings{10.2312:evs.20251087,
booktitle = {EuroVis 2025 - Short Papers},
editor = {El-Assady, Mennatallah and Ottley, Alvitta and Tominski, Christian},
title = {{Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction}},
author = {Jeon, Hyeon and Lee, Hyunwook and Kuo, Yun-Hsin and Yang, Taehyun and Archambault, Daniel and Ko, Sungahn and Fujiwara, Takanori and Ma, Kwan-Liu and Seo, Jinwook},
year = {2025},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-282-0},
DOI = {10.2312/evs.20251087}
}