Reliable Visual Analytics with Dimensionality Reduction: Quality Evaluation and Interpretation of Projections
| dc.contributor.author | Jeon, Hyeon | en_US |
| dc.contributor.author | Fujiwara, Takanori | en_US |
| dc.contributor.author | Martins, Rafael M. | en_US |
| dc.contributor.editor | Isenberg, Tobias | en_US |
| dc.contributor.editor | Sedlmair, Michael | en_US |
| dc.contributor.editor | Tierny, Julien | en_US |
| dc.date.accessioned | 2025-05-26T07:11:11Z | |
| dc.date.available | 2025-05-26T07:11:11Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Dimensionality reduction (DR) is widely used for visual analytics, but the insights obtained from these visualizations may often be unreliable. For example, DR projections distort the intrinsic structure of high-dimensional data in ways that may not be obvious at first glance, potentially leading analysts to inaccurate interpretations. Even reliable visual patterns may be hard to interpret regarding what exactly they convey about the underlying data, due to the often severe compression from hundreds (or thousands) of dimensions down to the visual space. In this tutorial, we discuss how to enhance the reliability of visual analytics with DR by focusing on two perspectives: quality evaluations and interpretations. While the former helps users identify or create projections with fewer distortions, the latter provides a reliable method for deriving insights from those projections. By combining lecture and coding exercises, we expect our tutorial to provide a grounded basis for audiences to use DR in a more reliable manner | en_US |
| dc.description.sectionheaders | Tutorials | |
| dc.description.seriesinformation | EuroVis 2025 - Panels and Tutorials | |
| dc.identifier.doi | 10.2312/evt.20251118 | |
| dc.identifier.isbn | 978-3-03868-285-1 | |
| dc.identifier.pages | 3 pages | |
| dc.identifier.uri | https://doi.org/10.2312/evt.20251118 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/evt20251118 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Human-centered computing → Visual analytics; Mathematics of computing → Dimensionality reduction | |
| dc.subject | Human centered computing → Visual analytics | |
| dc.subject | Mathematics of computing → Dimensionality reduction | |
| dc.title | Reliable Visual Analytics with Dimensionality Reduction: Quality Evaluation and Interpretation of Projections | en_US |
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