Nonparametric Dimensionality Reduction Quality Assessment based on Sortedness of Unrestricted Neighborhood

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dc.contributor.authorPereira-Santos, Davien_US
dc.contributor.authorNeves, Tácito Trindade Araújo Tiburtinoen_US
dc.contributor.authorCarvalho, André C. P. L. F. deen_US
dc.contributor.authorPaulovich, Fernando V.en_US
dc.contributor.editorAngelini, Marcoen_US
dc.contributor.editorEl-Assady, Mennatallahen_US
dc.date.accessioned2023-06-10T06:09:10Z
dc.date.available2023-06-10T06:09:10Z
dc.date.issued2023
dc.description.abstractHigh-dimensional data are known to be challenging to explore visually. Dimensionality Reduction (DR) techniques are good options for making high-dimensional data sets more interpretable and computationally tractable. An inherent question regarding their use is how much relevant information is lost during the layout generation process. In this study, we aim to provide means to quantify the quality of a DR layout according to the intuitive notion of sortedness of the data points. For such, we propose a straightforward measure with Kendall t at its core to provide values in a standard and meaningful interval. We present sortedness and pairwise sortedness as suitable replacements over, respectively, trustworthiness and stress when assessing projection quality. The formulation, its rationale and scope, and experimental results show their strength compared to the state-of-the-art.en_US
dc.description.sectionheadersPatterns and Multidimensional Projections
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.identifier.doi10.2312/eurova.20231093
dc.identifier.isbn978-3-03868-222-6
dc.identifier.issn2664-4487
dc.identifier.pages31-36
dc.identifier.pages6 pages
dc.identifier.urihttps://doi.org/10.2312/eurova.20231093
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurova20231093
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 design and evaluation methods; Visual analytics; Computing methodologies -> Dimensionality reduction and manifold learning
dc.subjectHuman centered computing
dc.subjectVisualization design and evaluation methods
dc.subjectVisual analytics
dc.subjectComputing methodologies
dc.subjectDimensionality reduction and manifold learning
dc.titleNonparametric Dimensionality Reduction Quality Assessment based on Sortedness of Unrestricted Neighborhooden_US
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