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Issue No. 12 - Dec. (2014 vol. 20)
ISSN: 1077-2626
pp: 2545-2554
Fan Hong , Key Laboratory of Machine Perception (Ministry of Education), School of EECS
Chufan Lai , Key Laboratory of Machine Perception (Ministry of Education), School of EECS
Hanqi Guo , Key Laboratory of Machine Perception (Ministry of Education), School of EECS
Enya Shen , School of Computer Science, National University of Defense Technology, Changsha, China
Xiaoru Yuan , Key Laboratory of Machine Perception (Ministry of Education), School of EECS
Sikun Li , School of Computer Science, National University of Defense Technology, Changsha, China
ABSTRACT
In this paper, we present a novel feature extraction approach called FLDA for unsteady flow fields based on Latent Dirichlet allocation (LDA) model. Analogous to topic modeling in text analysis, in our approach, pathlines and features in a given flow field are defined as documents and words respectively. Flow topics are then extracted based on Latent Dirichlet allocation. Different from other feature extraction methods, our approach clusters pathlines with probabilistic assignment, and aggregates features to meaningful topics at the same time. We build a prototype system to support exploration of unsteady flow field with our proposed LDA-based method. Interactive techniques are also developed to explore the extracted topics and to gain insight from the data. We conduct case studies to demonstrate the effectiveness of our proposed approach.
INDEX TERMS
Data visualization, Feature extraction, Analytical models, Data models, Computational modeling, Feature extraction,Latent Dirichlet allocation (LDA), Flow visualization, Topic model
CITATION
Fan Hong, Chufan Lai, Hanqi Guo, Enya Shen, Xiaoru Yuan, Sikun Li, "FLDA: Latent Dirichlet Allocation Based Unsteady Flow Analysis", IEEE Transactions on Visualization & Computer Graphics, vol. 20, no. , pp. 2545-2554, Dec. 2014, doi:10.1109/TVCG.2014.2346416
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