2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)最新文献

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LDAV 2018 Preface
2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2018-10-01 DOI: 10.1109/ldav.2018.8739223
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引用次数: 0
DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives DPP-PMRF:对使用数据并行原语的概率图形模型优化的再思考
2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2018-09-13 DOI: 10.1109/LDAV.2018.8739239
Brenton Lessley, T. Perciano, Colleen Heinemann, David Camp, H. Childs, E. W. Bethel
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引用次数: 6
Lifted Wasserstein Matcher for Fast and Robust Topology Tracking 基于提升Wasserstein匹配器的快速鲁棒拓扑跟踪
2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Pub Date : 2018-08-17 DOI: 10.1109/LDAV.2018.8739196
Maxime Soler, Mélanie Plainchault, B. Conche, Julien Tierny
{"title":"Lifted Wasserstein Matcher for Fast and Robust Topology Tracking","authors":"Maxime Soler, Mélanie Plainchault, B. Conche, Julien Tierny","doi":"10.1109/LDAV.2018.8739196","DOIUrl":"https://doi.org/10.1109/LDAV.2018.8739196","url":null,"abstract":"This paper presents a robust and efficient method for tracking topological features in time-varying scalar data. Structures are tracked based on the optimal matching between persistence diagrams with respect to the Wasserstein metric. This fundamentally relies on solving the assignment problem, a special case of optimal transport, for all consecutive timesteps. Our approach relies on two main contributions. First, we revisit the seminal assignment algorithm by Kuhn and Munkres which we specifically adapt to the problem of matching persistence diagrams in an efficient way. Second, we propose an extension of the Wasserstein metric that significantly improves the geometrical stability of the matching of domain-embedded persistence pairs. We show that this geometrical lifting has the additional positive side-effect of improving the assignment matrix sparsity and therefore computing time. The global framework computes persistence diagrams and finds optimal matchings in parallel for every consecutive timestep. Critical trajectories are constructed by associating successively matched persistence pairs over time. Merging and splitting events are detected with a geometrical threshold in a post-processing stage. Extensive experiments on real-life datasets show that our matching approach is up to two orders of magnitude faster than the seminal Munkres algorithm. Moreover, compared to a modern approximation method, our approach provides competitive runtimes while guaranteeing exact results. We demonstrate the utility of our global framework by extracting critical point trajectories from various time-varying datasets and compare it to the existing methods based on associated overlaps of volumes. Robustness to noise and temporal resolution downsampling is empirically demonstrated.","PeriodicalId":241591,"journal":{"name":"2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131587489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 34
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