CDS&E-MSS: Topological Learning with Multiparameter Persistent Homology

CDS

• 批准号: 2324353
• 负责人: Peter Bubenik
• 金额: $ 22万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324353&HistoricalAwards=false
• 关键词: CDS& MSS; Topological; Learning; Multiparameter

Experimental data may have an underlying shape whose determination can be crucial for scientific advances. In many cases, the data is sufficiently complex that state-of-the-art methods do not provide an adequate summary of its shape. The subject of this project, topological data analysis, uses ideas from theoretical mathematics to address this challenge. Topological data analysis has been successful in settings where the shape varies as a single parameter changes, but is in need of further research in settings where multiple parameters vary. The goal of this project is to develop mathematical summaries of the shape of data in the multiple parameter setting that may be easily combined with other tools in data science. In summary, this project will use ideas in mathematics, computer science, and statistics to advance theory and develop tools that are of broad use to scientists and engineers. A conference at the University of Florida will be organized in order to advance careers of young STEM researchers. A main tool of topological data analysis, persistent homology is now well established. The subject of this project concerns a more advanced variant called multiparameter persistent homology. The goal of this project is to develop new tools for easily combining multiparameter persistent homology with statistics and machine learning. Several of the computational approaches to multiparameter persistent homology produce summaries which may be viewed as signed formal sums on a pointed metric space. The investigator has shown that these summaries, called generalized persistence diagrams, have a Wasserstein distance, and may be viewed as elements of the free Banach space on a pointed metric space. This project will produce continuous linear functionals for generalized persistence diagrams and develop a corresponding theory of functional analysis and optimal transport. The investigator’s persistence landscape is a nonlinear functional for persistence diagrams. This project will extend the persistence landscape to generalized persistence diagrams. When a topological signal is detected or a topological classification is constructed, researchers would like to use this to learn more about their data. This project will develop methods for selecting a sub-population responsible for a detected topological signal. It will also use deep neural network software to produce stable visualizations of the parts of the data responsible for a learned classification.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

实验数据可能有一个潜在的形状，其确定可能对科学进步至关重要。在许多情况下，数据足够复杂，以至于最先进的方法无法提供对其形状的充分总结。这个项目的主题是拓扑数据分析，它使用理论数学的思想来解决这一挑战。在形状随单个参数变化而变化的设置中，拓扑数据分析已经取得了成功，但在多参数变化的设置中，拓扑数据分析还需要进一步研究。这个项目的目标是开发多参数设置下的数据形状的数学摘要，这些摘要可以很容易地与数据科学中的其他工具相结合。总而言之，这个项目将利用数学、计算机科学和统计学中的思想来推进理论并开发对科学家和工程师具有广泛用途的工具。将在佛罗里达大学组织一次会议，以促进年轻的STEM研究人员的职业生涯。持久同调是拓扑数据分析的主要工具，现在已经得到了很好的证实。这个项目的主题涉及一种更高级的变体，称为多参数持久同调。这个项目的目标是开发新的工具，轻松地将多参数持久同源性与统计学和机器学习结合起来。多参数持久同调的几种计算方法产生了可被视为指向度量空间上的带符号形式和的总结。研究人员已经证明了这些被称为广义持久图的总结具有Wasserstein距离，并且可以被视为指向度量空间上的自由Banach空间的元素。这个项目将产生广义持续图的连续线性泛函，并发展相应的泛函分析和最优运输理论。研究人员的持久性图景是持久性图的非线性泛函。这个项目将把持久化环境扩展到泛化的持久化图表。当检测到拓扑信号或构建了拓扑分类时，研究人员想要利用这一点来了解更多关于他们的数据。该项目将开发用于选择负责检测到的拓扑信号的子总体的方法。它还将使用深度神经网络软件来产生负责学习分类的数据部分的稳定可视化。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。