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dc.contributor.authorVernier, Eduardo Faccinen_US
dc.contributor.authorComba, João L. D.en_US
dc.contributor.authorTelea, Alexandru C.en_US
dc.contributor.editorBorgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonen_US
dc.date.accessioned2021-06-12T11:01:23Z
dc.date.available2021-06-12T11:01:23Z
dc.date.issued2021
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14291
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14291
dc.description.abstractProjections aim to convey the relationships and similarity of high-dimensional data in a low-dimensional representation. Most such techniques are designed for static data. When used for time-dependent data, they usually fail to create a stable and suitable low dimensional representation. We propose two dynamic projection methods (PCD-tSNE and LD-tSNE) that use global guides to steer projection points. This avoids unstable movement that does not encode data dynamics while keeping t-SNE's neighborhood preservation ability. PCD-tSNE scores a good balance between stability, neighborhood preservation, and distance preservation, while LD-tSNE allows creating stable and customizable projections. We compare our methods to 11 other techniques using quality metrics and datasets provided by a recent benchmark for dynamic projections.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectDimensionality reduction and manifold learning
dc.titleGuided Stable Dynamic Projectionsen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersMultivariate Data and Dimension Reduction
dc.description.volume40
dc.description.number3
dc.identifier.doi10.1111/cgf.14291
dc.identifier.pages87-98


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  • 40-Issue 3
    EuroVis 2021 - Conference Proceedings

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