Quantitative Evaluation of Time-Dependent Multidimensional Projection Techniques

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dc.contributor.authorVernier, Eduardo Faccinen_US
dc.contributor.authorGarcia, Rafaelen_US
dc.contributor.authorSilva, Iron Prando daen_US
dc.contributor.authorComba, João L. D.en_US
dc.contributor.authorTelea, Alexandru C.en_US
dc.contributor.editorViola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatianaen_US
dc.date.accessioned2020-05-24T13:00:36Z
dc.date.available2020-05-24T13:00:36Z
dc.date.issued2020
dc.description.abstractDimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time-dependent multivariate datasets. There are, however, few studies and proposals that leverage on the concise power of expression of projections in the context of dynamic/temporal data. In this paper, we aim at providing an approach to assess projection techniques for dynamic data and understand the relationship between visual quality and stability. Our approach relies on an experimental setup that consists of existing techniques designed for time-dependent data and new variations of static methods. To support the evaluation of these techniques, we provide a collection of datasets that has a wide variety of traits that encode dynamic patterns, as well as a set of spatial and temporal stability metrics that assess the quality of the layouts. We present an evaluation of 9 methods, 10 datasets, and 12 quality metrics, and elect the best-suited methods for projecting time-dependent multivariate data, exploring the design choices and characteristics of each method. Additional results can be found in the online benchmark repository. We designed our evaluation pipeline and benchmark specifically to be a live resource, open to all researchers who can further add their favorite datasets and techniques at any point in the future.en_US
dc.description.number3
dc.description.sectionheadersDimension Reduction and Projections
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.13977
dc.identifier.issn1467-8659
dc.identifier.pages241-252
dc.identifier.urihttps://doi.org/10.1111/cgf.13977
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13977
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.subjectComputing methodologies
dc.subjectDimensionality reduction and manifold learning
dc.titleQuantitative Evaluation of Time-Dependent Multidimensional Projection Techniquesen_US
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