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.
View on arXiv@article{jeon2025_2506.14820,
title={ Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction },
author={ Hyeon Jeon and Hyunwook Lee and Yun-Hsin Kuo and Taehyun Yang and Daniel Archambault and Sungahn Ko and Takanori Fujiwara and Kwan-Liu Ma and Jinwook Seo },
journal={arXiv preprint arXiv:2506.14820},
year={ 2025 }
}