FLDA: Latent Dirichlet Allocation Based Unsteady Flow Analysis

被引:22
作者
Hong, Fan [1 ]
Lai, Chufan [1 ]
Guo, Hanqi [1 ,2 ]
Shen, Enya [3 ]
Yuan, Xiaoru [1 ,2 ]
Li, Sikun [3 ]
机构
[1] Peking Univ, Minist Educ, Key Lab Machine Percept, Sch EECS, Beijing, Peoples R China
[2] Peking Univ, Ctr Computat Sci & Engn, Beijing, Peoples R China
[3] Natl Univ Def Technol, Sch Comp Sci, Changsha, Hunan, Peoples R China
关键词
Flow visualization; Topic model; Latent Dirichlet allocation (LDA); INTERACTIVE EXPLORATION; VOLUME DATA; VISUALIZATION; FRAMEWORK; FEATURES;
D O I
10.1109/TVCG.2014.2346416
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
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.
引用
收藏
页码:2545 / 2554
页数:10
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