Exploring High-Dimensional Data by Pointwise Filtering of Low-Dimensional Embeddings

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dc.contributor.authorAtzberger, Danielen_US
dc.contributor.authorJobst, Adrianen_US
dc.contributor.authorScheibel, Willyen_US
dc.contributor.authorDöllner, Jürgenen_US
dc.contributor.editorHunter, Daviden_US
dc.contributor.editorSlingsby, Aidanen_US
dc.date.accessioned2024-09-09T05:45:09Z
dc.date.available2024-09-09T05:45:09Z
dc.date.issued2024
dc.description.abstractDimensionality reductions are a class of unsupervised learning algorithms that aim to find a lower-dimensional embedding for a high-dimensional dataset while preserving local and global structures. By representing a high-dimensional dataset as a twodimensional scatterplot, a user can explore structures within the dataset. However, dimensionality reductions inherit distortions that might result in false deductions. This work presents a visualization approach that combines a two-dimensional scatterplot derived from a dimensionality reduction with two pointwise filtering possibilities. Each point is associated with two pointwise metrics that quantify the correctness of its neighborhood and similarity to surrounding data points. By setting threshold for these two metrics, the user is supported in several scatterplot analytics tasks, e.g., class separation and outlier detection. We apply our visualization to a text corpus to detect interesting data points visually and discuss the findings.en_US
dc.description.sectionheadersComputer Graphics
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20241224
dc.identifier.isbn978-3-03868-249-3
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/cgvc.20241224
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/cgvc20241224
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Information visualization; Visual analytics
dc.subjectHuman centered computing → Information visualization
dc.subjectVisual analytics
dc.titleExploring High-Dimensional Data by Pointwise Filtering of Low-Dimensional Embeddingsen_US
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