Collaborative Research:CISE-ANR:CIF:Small:Learning from Large Datasets - Application to Multi-Subject fMRI Analysis

合作研究：CISE-ANR:CIF:Small：从大数据集中学习 - 多对象 fMRI 分析的应用

• 批准号: 2316420
• 负责人: Tulay Adali
• 金额: $ 39.98万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316420&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; ANR; CIF

In many disciplines today, there is an increasing availability of multiple and complementary data associated with a given problem, and the main challenge is extracting and effectively summarizing the relevant information from these large number of datasets. Joint decomposition of these datasets, arranged as matrices or tensors, provides an attractive solution to data fusion by letting them fully interact and inform each other and yields factor matrices that are directly interpretable, where the resulting factors (components) are directly associated with quantities of interest. This research will provide a powerful solution for inference from large-scale data by effectively summarizing the heterogeneity in large datasets through the definition of homogeneous subspaces such that components within a subspace are highly dependent. The success of the methods will be demonstrated through identification of homogeneous subgroups of subjects from neuroimaging data, thus enabling personalized medicine whose goal is to tailor intervention strategies for a given individual. Effectively summarizing information in large-scale datasets is at the heart of many of today's challenging problems, hence the new set of tools will impact numerous areas in science and technology, including those in medical imaging, remote sensing, image/video processing, communications, and social networks. Independent vector analysis (IVA) and coupled tensor factorizations are two powerful ways for working with spatio-temporal data, each exploiting the structural/dependence information through different mechanisms. They also provide strong uniqueness guarantees, which is key for interpretability. This project leverages the complementary strengths of IVA and coupled tensor decompositions to develop a powerful framework for joint analysis/fusion of a large number of datasets through automated identification of homogeneous subspaces along with the components within these subspaces. This is accomplished by initially developing effective solutions to the problem with IVA and with coupled tensor decompositions, working in parallel. Then, in a second stage, the connections between these two approaches are established, both in terms of methods and uniqueness conditions, to develop a methodology that leverages the strengths of both approaches. The emphasis on uniqueness and interpretability of the solutions together with an application to a challenging dataset will ensure that the methods, as well as the developed theoretical foundations, are not only complete but also practically useful. Another important aspect of the work is the establishing of bridges across two communities that do not necessarily communicate. The work will demonstrate that statistically and algebraically motivated approaches to data fusion are not in competition with each other but have important complementary aspects that can be effectively leveraged. In addition, a clear view of their connections as well as differences enables fair comparisons of all methods clearly highlighting their abilities together with their limitations. This will help establish a solid and well-balanced foundation for the growing fields of data science and machine learning.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.

在当今的许多学科中，与给定问题相关的多个和互补数据的可用性越来越大，主要挑战是提取并有效地总结这些大量数据集中的相关信息。这些数据集的联合分解（以矩阵或张量为张量）通过让它们完全相互作用并相互通知并产生可解释的因子矩阵，从而为数据融合提供了有吸引力的解决方案，而所产生的因素（组件）与兴趣数量直接相关。这项研究将通过定义均匀的子空间来有效地汇总大数据集中的异质性，从而为从大规模数据提供有力的解决方案，从而高度依赖于子空间中的组件。这些方法的成功将通过从神经成像数据中识别出受试者的均质亚组来证明，从而实现了个性化医学，其目标是为给定个人量身定制干预策略。有效地总结大规模数据集中的信息是当今许多具有挑战性的问题的核心，因此，新的工具集将影响科学和技术的许多领域，包括医学成像，遥感，图像/视频处理，通信，通信和社交网络的工具。独立的矢量分析（IVA）和耦合张量因子化是使用时空数据的两种强大方法，每个方法都通过不同的机制利用结构/依赖信息。它们还提供了强大的独特性保证，这是解释性的关键。该项目利用IVA和耦合张量分解的互补优势来开发有力的框架，以通过自动识别均匀子空间以及这些子空间中的组件来自动识别大量数据集的联合分析/融合。这是通过最初针对IVA问题和耦合张量分解的有效解决方案（并行工作）来实现的。然后，在第二阶段，在方法和唯一条件方面都建立了这两种方法之间的联系，以开发一种利用这两种方法优势的方法。强调解决方案的唯一性和解释性以及对具有挑战性的数据集的应用将确保方法以及开发的理论基础不仅完整，而且实际上也很有用。这项工作的另一个重要方面是在两个不一定进行交流的社区建立桥梁。这项工作将证明，统计和代数动机的数据融合方法并不彼此竞争，而是具有可以有效利用的重要互补方面。此外，对它们的联系以及差异的清晰视图可以使所有方法的公平比较显然强调了他们的能力以及局限性。这将有助于为数据科学和机器学习的不断增长领域建立坚实而均衡的基础。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来支持。