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

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• 批准号: 1065107
• 负责人: David Thompson
• 金额: $ 28.03万
• 依托单位: Mississippi State University
• 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=1065107&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Rangapson随着计算机能力的不断增加，模拟的复杂性也随之增加，从而产生前所未有的规模的数据集。 如果没有有效的分析工具，这些大规模模拟的结果就不能得到最充分的利用。本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，该机器学习利用了数据的有效的基于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来执行两项不同的任务：识别感兴趣的区域和增强特征检测算法的鲁棒性。这一努力的主要成果是实现了一个探索大型数据集的框架。此外，这项工作正在将机器学习中的大量工作引入可视化领域。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，并促进在许多科学和工程领域的有利发现。这里开发的多分辨率技术将采用双重策略。首先，半监督学习的基础上与领域专家的培训是用来制定战略，选择性的空间和时间细化的数据。 构造分类器以将粗分辨率特征检测的输出（即区域）标记为感兴趣或不感兴趣。然后，在最精细的尺度上，识别出包含感兴趣特征的感兴趣的局部数据块以供进一步分析。其次，几个局部特征检测算法，或弱分类器，被组合成一个单一的，更强大的复合分类器，使用自适应增强，或AdaBoost，和一个数据自适应变体称为CAVIAR，有利于验证的特征检测。理想情况下，复合分类器结合了所有弱分类器中最好的，因为它们响应于潜在的物理信号。本研究通过应用现有的局部检测算法来可视化湍流场中的旋涡，从而证明了这些方法的有效性。