Stress Maps: Analysing Local Phenomena in Dimensionality Reduction Based Visualisations

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dc.contributor.authorSeifert, Christinen_US
dc.contributor.authorSabol, Vedranen_US
dc.contributor.authorKienreich, Wolfgangen_US
dc.contributor.editorJoern Kohlhammer and Daniel Keimen_US
dc.date.accessioned2014-01-27T15:28:29Z
dc.date.available2014-01-27T15:28:29Z
dc.date.issued2010en_US
dc.description.abstractChallenges in Visual Analytics frequently involve massive repositories, which do not only contain a large number of information artefacts, but also a high number of relevant dimensions per artefact. Dimensionality reduction algorithms are commonly used to transform high-dimensional data into low- dimensional representations which are suitable for visualisation purposes. For example, Information Landscapes visualise high-dimensional data in two dimensions using distance-preserving projection methods. The inaccuracies introduced by such methods are usually expressed through a global stress measure which does not provide insight into localised phenomena. In this paper, we propose the use of Stress Maps, a combination of heat maps and information landscapes, to support algorithm development and optimization based on local stress measures. We report on an application of Stress Maps to a scalable text projection algorithm and describe two categories of problems related to localised stress phenomena which we have identified using the proposed method.en_US
dc.description.seriesinformationEuroVAST 2010: International Symposium on Visual Analytics Science and Technologyen_US
dc.identifier.isbn978-3-905673-74-6en_US
dc.identifier.urihttps://doi.org/10.2312/PE/EuroVAST/EuroVAST10/013-018en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.8 [Computer Graphics]: Applications-en_US
dc.titleStress Maps: Analysing Local Phenomena in Dimensionality Reduction Based Visualisationsen_US
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