cPro: Circular Projections Using Gradient Descent

dc.contributor.authorBuchmüller, Raphaelen_US
dc.contributor.authorJäckl, Bastianen_US
dc.contributor.authorBehrisch, Michaelen_US
dc.contributor.authorKeim, Daniel A.en_US
dc.contributor.authorDennig, Frederik L.en_US
dc.contributor.editorEl-Assady, Mennatallahen_US
dc.contributor.editorSchulz, Hans-Jörgen_US
dc.date.accessioned2024-05-21T08:30:05Z
dc.date.available2024-05-21T08:30:05Z
dc.date.issued2024
dc.description.abstractTypical projection methods such as PCA or MDS rely on mapping data onto an Euclidean space, limiting the design of resulting visualizations to lines, planes, or cubes and thus may fail to capture the intrinsic non-linear relationships within data, resulting in inefficient use of two-dimensional space. We introduce the novel projection technique -cPro-, which aligns high-dimensional data onto a circular layout. We apply gradient descent, an adaptable optimization technique to efficiently reduce a customized loss function. We use selected distance measures to reduce high data dimensionality and reveal patterns on a two-dimensional ring layout. We evaluate our approach compared to 1D and 2D MDS and discuss further use cases and potential extensions. cPro enables the design of novel visualization techniques that employ semantic distances on a circular layout.en_US
dc.description.sectionheadersVisual Analytics Methods and Approaches
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.identifier.doi10.2312/eurova.20241111
dc.identifier.isbn978-3-03868-253-0
dc.identifier.pages6 pages
dc.identifier.urihttps://doi.org/10.2312/eurova.20241111
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/eurova20241111
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 → Visualization techniques
dc.subjectHuman centered computing → Visualization techniques
dc.titlecPro: Circular Projections Using Gradient Descenten_US
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