New Geometric Methods of Mixture Models for Interactive Visualization

交互式可视化混合模型的新几何方法

• 批准号: 0936948
• 负责人: Xiaolong (Luke) Zhang
• 金额: $ 49.8万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0936948&HistoricalAwards=false
• 关键词: New; Geometric; Methods; Mixture; Models

This research project will extend the theoretical foundations of mixture modeling for statistical learning by novel mathematical tools that can probe into the precise geometry of mixture models. Based on the theoretical results, the investigators will develop new approaches to clustering, dimension reduction, variable selection, and temporal analysis. These methods will open promising paths for interactively visualizing complex data and for data summarization. A suite of statistical tools will be integrated as the technical backbone into a new visualization system. Applications to very large-scale, high dimensional, and temporally evolving data will be explored. The principal investigators, with complementary backgrounds in theoretical statistics, computational statistics, and information visualization, will also work with colleagues across multiple departments at Penn State University to test their methods and prototype systems using real-world data sets.In a plethora of scientific and engineering areas with direct and tremendous impacts on our everyday life, such as extreme weather prediction and manufacturing engineering design, researchers are facing gigantic amount of data with great complexity in terms of dimensionality, data types, statistical dependence, and temporal variations. Visualization has played important roles in support of analyzing complex data. Visualization systems help users increase available spatial and cognitive resources, improve searching, enhance pattern recognition, and ultimately make sense of abstract phenomena. This research project aims at fundamentally advancing the mathematical core of visualization systems. The investigators take a probabilistic framework to model data, specifically the mixture model. Mixture modeling provides a highly flexible and theoretically solid basis for summarizing data and automatically extracting patterns from data. This project will develop theories and algorithms for mixture modeling and exploit them to construct new statistical learning and data mining techniques. These statistical methods will thoroughly change the ways visualization systems are designed, offering more functions as well as better functions. Software packages for advanced methods of statistical learning and interactive visualization will be developed and distributed for public use. The proposed research on data visualization and modeling techniques are expected to affect a wide range of fields in science, engineering, and commerce. The applications to hurricane forecast and engineering design can deeply influence our daily life.

该研究项目将通过新的数学工具扩展混合模型的理论基础，以探索混合模型的精确几何形状。 基于理论结果，研究人员将开发新的方法来聚类，降维，变量选择和时间分析。 这些方法将为复杂数据的交互式可视化和数据汇总开辟有前途的道路。 将把一套统计工具作为技术骨干纳入新的可视化系统。非常大规模的，高维的，和时间上不断变化的数据的应用程序将进行探索。 主要研究人员在理论统计、计算统计和信息可视化方面具有互补的背景，他们还将与宾夕法尼亚州立大学多个部门的同事合作，使用真实世界的数据集测试他们的方法和原型系统。在众多对我们日常生活有直接和巨大影响的科学和工程领域，如极端天气预测和制造工程设计，研究人员面临着海量的数据，这些数据在维度、数据类型、统计依赖性和时间变化方面具有很大的复杂性。 可视化在支持复杂数据分析方面发挥了重要作用。 可视化系统帮助用户增加可用的空间和认知资源，改善搜索，增强模式识别，并最终使抽象现象有意义。 该研究项目旨在从根本上推进可视化系统的数学核心。研究人员采用概率框架来建模数据，特别是混合模型。混合建模为总结数据和从数据中自动提取模式提供了高度灵活和理论上坚实的基础。本计画将发展混合模型的理论与演算法，并利用它们来建构新的统计学习与资料探勘技术。 这些统计方法将彻底改变可视化系统的设计方式，提供更多的功能以及更好的功能。 将开发和分发统计学习和交互式可视化高级方法软件包，供公众使用。 数据可视化和建模技术的拟议研究预计将影响科学，工程和商业的广泛领域。 它在飓风预报和工程设计中的应用对我们的日常生活产生了深远的影响。