Necessary but not Sufficient: Limitations of Projection Quality Metrics
| dc.contributor.author | Machado, Alister | en_US |
| dc.contributor.author | Behrisch, Michael | en_US |
| dc.contributor.author | Telea, Alexandru | en_US |
| dc.contributor.editor | Aigner, Wolfgang | en_US |
| dc.contributor.editor | Andrienko, Natalia | en_US |
| dc.contributor.editor | Wang, Bei | en_US |
| dc.date.accessioned | 2025-05-26T06:36:13Z | |
| dc.date.available | 2025-05-26T06:36:13Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | High-dimensional data analysis often uses dimensionality reduction (DR, also called projection) to map data patterns to human-digestible visual patterns in a 2D scatterplot. Yet, DR methods may fail to show true data patterns and/or create visual patterns that do not represent any data patterns. Projection Quality Metrics (PQMs) are used as objective measures to gauge the above process: the higher a projection's scores in PQMs, the more it is deemed faithful to the data it represents. We show that, while PQMs can be used as exclusion criteria - low values usually mean poor projections - the converse does not always hold. For this, we develop a technique to automatically generate projections that score similar or even higher PQM values than projections created by well-known techniques, but show different, often confusing, visual patterns. Our results show that accepted PQMs cannot be used as an exclusive way to tell whether a projection yields accurate and interpretable visual patterns - in this sense, PQMs play a role akin to that of summary statistics in exploratory data analysis. We also show that not all studied metrics can be fooled equally well, suggesting a ranking of metrics in their ability to reliably capture quality. | en_US |
| dc.description.sectionheaders | Dimensionality Reduction and High-Dimensional Data | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.identifier.doi | 10.1111/cgf.70101 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 12 pages | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.70101 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.1111/cgf70101 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Mathematics of computing → Dimensionality reduction; Computing methodologies → Machine learning; Humancentered computing → Information visualization | |
| dc.subject | Mathematics of computing → Dimensionality reduction | |
| dc.subject | Computing methodologies → Machine learning | |
| dc.subject | Humancentered computing → Information visualization | |
| dc.title | Necessary but not Sufficient: Limitations of Projection Quality Metrics | en_US |