Journal
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Volume 21, Issue 9, Pages 1072-1086Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2015.2410278
Keywords
Ensemble visualization; uncertainty quantification; uncertainty visualization; multidimensional data visualization
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Funding
- National High Technology Research and Development Program of China [2012AA12090]
- Major Program of National Natural Science Foundation of China [61232012, 61422211]
- Zhejiang Provincial Natural Science Foundation of China [LR13F020001, LR14F020002]
- National Science Foundation [NSF1117871]
- Pacific Northwest National Laboratory under U.S. Department of Energy [DE-AC05-76RL01830]
- National Science Foundation of China [61379076]
- Program for New Century Excellent Talents in University of China [NCET-12-1087]
- NUS-ZJU SeSama center
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1117871] Funding Source: National Science Foundation
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This paper presents an efficient visualization and exploration approach for modeling and characterizing the relationships and uncertainties in the context of a multidimensional ensemble dataset. Its core is a novel dissimilarity-preserving projection technique that characterizes not only the relationships among the mean values of the ensemble data objects but also the relationships among the distributions of ensemble members. This uncertainty-aware projection scheme leads to an improved understanding of the intrinsic structure in an ensemble dataset. The analysis of the ensemble dataset is further augmented by a suite of visual encoding and exploration tools. Experimental results on both artificial and real-world datasets demonstrate the effectiveness of our approach.
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