G&V: Medium: Collaborative Research: Large Data Visualization Using An Interactive Machine Learning Framework

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• 批准号: 1065081
• 负责人: Anand Rangarajan
• 金额: $ 30.34万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1065081&HistoricalAwards=false
• 关键词: G& Medium; Collaborative; Research; Large

Abstract - Machiraju, Rangarajan, and ThompsonAs computer power continues to increase, the complexity of simulations also increases thereby producing datasets of unprecedented size. Without effective analysis tools, results from these large-scale simulations cannot be utilized to their fullest extent. This research addresses the problem of large-data visualization and exploration by employing interactive multi-scale machine learning, which exploits an efficient feature-based, multi-resolution representation of the data. The investigators are leveraging methods from the field of machine learning to perform two distinct tasks: identify regions of interest and enhance robustness of feature detection algorithms. The primary outcome of this effort is the realization of a framework for exploring large datasets. Further, this work is introducing a large body of work in machine learning to the field of visualization. Successful completion of this research will help overcome the brittleness of existing visualization methods and foster expedient discovery in many areas of science and engineering.The multi-resolution techniques developed here will employ a two-fold strategy. First, semi-supervised learning based on training with the domain expert is used to develop strategies for selective spatial and temporal refinement of the data. A classifier is constructed to tag the output of the coarse resolution feature detection (i.e. regions) as either interesting or not interesting. Then at the finest scale, interesting local data chunks containing features of interest are identified for further analysis. Second, several local feature detection algorithms, or weak classifiers, are combined into a single, more robust compound classifier using adaptive boosting, or AdaBoost, and a data adaptive variant called CAVIAR that facilitates validated feature detection. Ideally, the compound classifier combines the best of all weak classifiers as they respond to the underlying physical signal. This research is demonstrating the effectiveness of these methods by applying existing local detection algorithms for visualizing vortices in turbulent flow fields.

摘要-Machiraju，Rangarajan和Thompsonas计算机功率继续增加，模拟的复杂性也增加，从而产生了前所未有的大小的数据集。 没有有效的分析工具，这些大规模模拟的结果将无法达到最大程度。这项研究通过采用交互式多尺度机器学习来解决大数据可视化和探索的问题，该机器学习利用了基于数据的有效特征的多分辨率表示。研究人员正在利用机器学习领域的方法执行两个不同的任务：确定感兴趣的区域并增强特征检测算法的鲁棒性。这项工作的主要结果是实现了探索大型数据集的框架。此外，这项工作将机器学习中的大量工作引入了可视化领域。这项研究的成功完成将有助于克服现有可视化方法的脆弱性，并在科学和工程的许多领域促进平稳发现。此处开发的多分辨率技术将采用两倍的策略。首先，基于与域专家的培训进行的半监督学习用于制定选择性空间和时间精致数据的策略。 分类器的构建是为了将粗分辨率特征检测（即区域）的输出标记为有趣或不有趣的输出。然后，在最佳规模上，确定了包含感兴趣特征的有趣的本地数据块，以进行进一步分析。其次，使用自适应增强或Adaboost的几种局部特征检测算法或弱分类器将其合并为一个更健壮的复合分类器，以及一个称为CAVIAR的数据自适应变体，可促进经过验证的功能检测。理想情况下，复合分类器将所有弱分类器中的最好的分类器响应在响应基本的物理信号时。这项研究通过应用现有的局部检测算法来可视化湍流场中的涡旋，从而证明了这些方法的有效性。