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|a 10.1109/TVCG.2021.3125576
|2 doi
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|a pubmed24n1109.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Espadoto, Mateus
|e verfasserin
|4 aut
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| 245 |
1 |
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|a UnProjection
|b Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 05.04.2023
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|a Date Revised 05.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection - the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this article we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method's utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization
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|a Journal Article
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|a Appleby, Gabriel
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Suh, Ashley
|e verfasserin
|4 aut
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| 700 |
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|a Cashman, Dylan
|e verfasserin
|4 aut
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| 700 |
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|a Li, Mingwei
|e verfasserin
|4 aut
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| 700 |
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|a Scheidegger, Carlos
|e verfasserin
|4 aut
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| 700 |
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|a Anderson, Erik W
|e verfasserin
|4 aut
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| 700 |
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|a Chang, Remco
|e verfasserin
|4 aut
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| 700 |
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|a Telea, Alexandru C
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 29(2023), 2 vom: 08. Feb., Seite 1559-1572
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|g volume:29
|g year:2023
|g number:2
|g day:08
|g month:02
|g pages:1559-1572
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|u http://dx.doi.org/10.1109/TVCG.2021.3125576
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