Efficient Computation of Generalized Persistence Diagrams

广义持久图的高效计算

• 批准号: 2324632
• 负责人: Dmitriy Morozov
• 金额: $ 16万
• 依托单位: International Computer Science Institute
• 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: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324632&HistoricalAwards=false
• 关键词: Efficient; Computation; Generalized; Persistence; Diagrams

Topological data analysis (TDA) brings techniques from algebraic topology to the applied domains. It emphasizes methods that are stable, and as such resilient to noise in the data, as well as methods that are computationally efficient, and as such practical across a range of applications. Over the last two decades, TDA techniques have found applications in many domains, including biochemistry, cosmology, materials science, neuroscience, climate research, and many others. One significant limitation that practitioners encounter is that one of the main methods in TDA, persistent homology, is limited to studying one-parameter data, i.e., measurements of a single quantity. The focus of this project is that in practice, multiple measurements are available, and it is precisely the correlation between them that will be used to reveal important features of the problem. Software for practitioners using this methodology will be developed and the project will include graduate student training in topological data analysis.Recently, generalized persistent homology was introduced. It interprets persistence as a Moebius inversion of a certain function derived from the changes in topology of the data across parameters. The construction generalizes all the properties of 1-parameter persistence needed in applications, including stability and the particular structure of the diagrams used in machine learning and statistical pipelines. This project will develop a software implementation for computing generalized persistence diagrams, a crucial gap in this research program and the missing bridge between theory and applications.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.

拓扑数据分析(TDA)将代数拓扑学的技术引入到应用领域。它强调方法是稳定的，因此对数据中的噪声具有弹性，以及方法是计算高效的，因此在一系列应用程序中都是实用的。在过去的二十年里，TDA技术在许多领域得到了应用，包括生物化学、宇宙学、材料科学、神经科学、气候研究等。实践者遇到的一个重要限制是，TDA的主要方法之一，持久同源性，仅限于研究单参数数据，即对单量的测量。这个项目的重点是，在实践中，可以使用多种测量方法，而正是它们之间的相关性将被用来揭示问题的重要特征。将为使用这种方法的从业者开发软件，该项目将包括拓扑数据分析方面的研究生培训。最近，广义持久同调被引入。它将持久性解释为某一函数的Moebius逆，该函数源于参数间数据拓扑的变化。该结构概括了应用程序中所需的单参数持久性的所有性质，包括稳定性以及机器学习和统计管道中使用的图表的特殊结构。这个项目将开发一个用于计算广义持续图的软件实现，这是该研究计划中的一个关键差距，也是理论和应用之间缺失的桥梁。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。