Explaining Neighborhood Preservation for Multidimensional Projections

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dc.contributor.authorMartins, Rafael Messiasen_US
dc.contributor.authorMinghim, Rosaneen_US
dc.contributor.authorTelea, Alexandru C.en_US
dc.contributor.editorRita Borgo and Cagatay Turkayen_US
dc.date.accessioned2015-09-16T05:08:53Z
dc.date.available2015-09-16T05:08:53Z
dc.date.issued2015en_US
dc.description.abstractDimensionality reduction techniques are the tools of choice for exploring high-dimensional datasets by means of low-dimensional projections. However, even state-of-the-art projection methods fail, up to various degrees, in perfectly preserving the structure of the data, expressed in terms of inter-point distances and point neighborhoods. To support better interpretation of a projection, we propose several metrics for quantifying errors related to neighborhood preservation. Next, we propose a number of visualizations that allow users to explore and explain the quality of neighborhood preservation at different scales, captured by the aforementioned error metrics.We demonstrate our exploratory views on three real-world datasets and two state-of-the-art multidimensional projection techniques.en_US
dc.description.sectionheadersVisualisation and Analyticsen_US
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)en_US
dc.identifier.doi10.2312/cgvc.20151234en_US
dc.identifier.isbn978-3-905674-94-1en_US
dc.identifier.pages7-14en_US
dc.identifier.urihttps://doi.org/10.2312/cgvc.20151234en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.8 [Computer Graphics]en_US
dc.subjectPicture/Image Generationen_US
dc.subjectViewing algorithmsen_US
dc.subjectI.4.10 [Computer Graphics]en_US
dc.subjectImage Representationen_US
dc.subjectMultidimensionalen_US
dc.titleExplaining Neighborhood Preservation for Multidimensional Projectionsen_US
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