Explaining Neighborhood Preservation for Multidimensional Projections (Unknown language)

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Dimensionality 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.

Table of contents conference proceedings

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1
Multivariate Hybrid Visualisation of Ornithological Sensor Data
Roberts, Richard C. / Laramee, Robert S. / Jones, Mark W. | 2015
7
Explaining Neighborhood Preservation for Multidimensional Projections
Martins, Rafael Messias / Minghim, Rosane / Telea, Alexandru C. | 2015
27
GKS-94 to SVG: Some Reflections on the Evolution of Standards for 2D Graphics
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55
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69
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77
Extending the Scaffold Hunter Visualization Toolkit with Interactive Heatmaps
Sturm, Werner / Schäfer, Till / Schreck, Tobias / Holzinger, Andreas / Ullrich, Torsten | 2015
87
Using Sketching to Control Heterogeneous Groups
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