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dc.contributor.authorPezzotti, Nicolaen_US
dc.contributor.authorHöllt, Thomasen_US
dc.contributor.authorLelieveldt, Boudewijn P. F.en_US
dc.contributor.authorEisemann, Elmaren_US
dc.contributor.authorVilanova, Annaen_US
dc.contributor.editorKwan-Liu Ma and Giuseppe Santucci and Jarke van Wijken_US
dc.date.accessioned2016-06-09T09:32:33Z
dc.date.available2016-06-09T09:32:33Z
dc.date.issued2016en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.12878en_US
dc.identifier.urihttps://diglib.eg.org:443/handle/10
dc.description.abstractIn recent years, dimensionality-reduction techniques have been developed and are widely used for hypothesis generation in Exploratory Data Analysis. However, these techniques are confronted with overcoming the trade-off between computation time and the quality of the provided dimensionality reduction. In this work, we address this limitation, by introducing Hierarchical Stochastic Neighbor Embedding (Hierarchical-SNE). Using a hierarchical representation of the data, we incorporate the wellknown mantra of Overview-First, Details-On-Demand in non-linear dimensionality reduction. First, the analysis shows an embedding, that reveals only the dominant structures in the data (Overview). Then, by selecting structures that are visible in the overview, the user can filter the data and drill down in the hierarchy. While the user descends into the hierarchy, detailed visualizations of the high-dimensional structures will lead to new insights. In this paper, we explain how Hierarchical-SNE scales to the analysis of big datasets. In addition, we show its application potential in the visualization of Deep-Learning architectures and the analysis of hyperspectral images.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.0 [Computer Graphics]en_US
dc.subjectGeneralen_US
dc.titleHierarchical Stochastic Neighbor Embeddingen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.sectionheadersHigh-Dimensional Dataen_US
dc.description.volume35en_US
dc.description.number3en_US
dc.identifier.doi10.1111/cgf.12878en_US
dc.identifier.pages021-030en_US


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