Kristian Nybo

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Nonlinear dimensionality reduction methods are often used to visualize high-dimensional data, although the existing methods have been designed for other related tasks such as manifold learning. It has been difficult to assess the quality of visualizations since the task has not been well-defined. We give a rigorous definition for a specific visualization(More)
We have recently introduced rigorous goodness criteria for information visualization by posing it as a visual neighbor retrieval problem, where the task is to find proximate high-dimensional data based only on a low-dimensional display. Standard information retrieval criteria such as precision and recall can then be used for information visualization, and(More)
Graphs are central representations of information in many domains including biological and social networks. Graph visualization is needed for discovering underlying structures or patterns within the data, for example communities in a social network, or interaction patterns between protein complexes. Existing graph visualization methods, however, often fail(More)
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