Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TVCG.2014.2346416 | IEEE Trans. Vis. Comput. Graph. |
Keywords | Field | DocType |
flow topics,feature extraction methods,interactive techniques,latent dirichlet allocation,probabilistic assignment,prototype system,unsteady flow analysis,latent dirichlet allocation (lda),novel feature extraction approach,flda,feature extraction,data visualisation,topic model,flow visualization,flow field,probability,computational modeling,data models,data visualization | Data modeling,Data mining,Data visualization,Latent Dirichlet allocation,Computer science,Flow (psychology),Feature extraction,Topic model,Probabilistic logic,Flow visualization | Journal |
Volume | Issue | ISSN |
20 | 12 | 1077-2626 |
Citations | PageRank | References |
10 | 0.78 | 34 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Hong | 1 | 40 | 4.32 |
| Chufan Lai | 2 | 27 | 3.69 |
| Hanqi Guo | 3 | 338 | 23.06 |
| Enya Shen | 4 | 20 | 2.60 |
| Xiaoru Yuan | 5 | 1157 | 70.28 |
| Sikun Li | 6 | 224 | 44.71 |