FODAVA-Lead: Dimension Reduction and Data Reduction: Foundations for Visualization

FODAVA-Lead：降维和数据缩减：可视化的基础

• 批准号: 0808863
• 负责人: Haesun Park
• 金额: $ 300万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0808863&HistoricalAwards=false
• 关键词: FODAVA; Lead; Dimension; Reduction; Data

FODAVA-Lead: Dimension Reduction and Data Reduction: Foundations for VisualizationThe FODAVA (Foundations of Data Analysis and Visualization) Lead research team at the Georgia Institute of Technology provides unified expertise in the critical areas for providing leadership of the FODAVA effort, including machine learning and computational statistics, information visualization, massive-dataset algorithms and data structures, and optimization theory. The team is focused on the fundamental theory and approaches to make breakthroughs in data representations and transformations. The work is directed along the two main axes of scale reduction, data reduction and dimension reduction, to allow human analysts to absorb and penetrate modern large-scale high-dimensional datasets cognitively and visually.In the area of dimension reduction, the team is extending the theory of automatic feature selection by sparse recovery, developing effective methods for manifold dimension reduction in terms of differential operators, developing new scalable manifold methods using convex-concave saddle-point approaches, and creating dimension reduction methods which incorporate constraints that increase their understandability, such as preserving the data's cluster structure. In the area of data reduction, the team is developing multi-resolution data approximation schemes using hierarchical data structures and multipole-like expansions, approaches for analyzing data of heterogeneous data quality using convolution kernel approaches, and approaches for automatic anomaly cleaning and detection using Lp estimation. The results of this research impact the theory and practice of data analysis, as well as that of information visualization, in particular through the Visual Analytics Digital Library, integration of the resulting methodologies into existing visual analytics systems, and a series of workshops. Undergraduates, under-represented groups, and graduate students are educated in this new interdisciplinary area respectively through Georgia Tech's Threads model, FACES effort, and innovative PhD introductory course emphasizing cross-disciplinary research and new PhD program in Computational Science and Engineering.

FODAVA-Lead：降维和数据缩减：可视化的基础佐治亚理工学院的 FODAVA（数据分析和可视化基础）领导研究团队在关键领域提供统一的专业知识，以领导 FODAVA 工作，包括机器学习和计算统计学、信息可视化、海量数据集算法和数据结构以及优化理论。 该团队专注于基础理论和方法的研究，以在数据表示和转换方面取得突破。 这项工作围绕尺度缩减、数据缩减和降维两个主轴展开，让人类分析师能够在认知和视觉上吸收和渗透现代大规模高维数据集。在降维领域，团队正在扩展通过稀疏恢复进行自动特征选择的理论，根据微分算子开发流形降维的有效方法，使用凸凹鞍点方法开发新的可扩展流形方法，并创建包含增加其可理解性的约束的降维方法，例如保留数据的簇结构。在数据缩减领域，该团队正在开发使用分层数据结构和类多极展开的多分辨率数据近似方案、使用卷积核方法分析异构数据质量的数据的方法以及使用Lp自动异常清理和检测的方法估计。 这项研究的结果影响了数据分析以及信息可视化的理论和实践，特别是通过视觉分析数字图书馆、将所得方法集成到现有的视觉分析系统以及一系列研讨会。 本科生、代表性不足的群体和研究生分别通过佐治亚理工学院的 Threads 模型、FACES 工作以及强调跨学科研究和计算科学与工程新博士课程的创新博士入门课程接受这一新跨学科领域的教育。