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: 2316421
• 负责人: Vince Calhoun
• 金额: $ 19.85万
• 依托单位: Georgia State University Research Foundation, Inc.
• 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=2316421&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种互补数据的可用性越来越多，主要挑战是从这些大量数据集中提取并有效总结相关信息。这些数据集的联合分解（排列为矩阵或张量）通过让它们充分交互和相互告知，为数据融合提供了一种有吸引力的解决方案，并产生可直接解释的因子矩阵，其中生成的因子（分量）与数据的数量直接相关。兴趣。这项研究将为大规模数据的推理提供强大的解决方案，通过同质子空间的定义有效地总结大型数据集中的异质性，使得子空间内的组件高度依赖。这些方法的成功将通过从神经影像数据中识别受试者的同质亚组来证明，从而实现个性化医疗，其目标是为特定个体量身定制干预策略。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，因此这套新工具将影响科学技术的众多领域，包括医学成像、遥感、图像/视频处理、通信、独立向量分析 (IVA) 和耦合张量分解是处理时空数据的两种强大方法，每种方法都通过不同的机制利用结构/依赖性信息。它们还提供强大的唯一性保证，这是可解释性的关键。该项目利用 IVA 和耦合张量分解的互补优势，开发一个强大的框架，通过自动识别同质子空间以及这些子空间内的组件来联合分析/融合大量数据集。这是通过首先使用 IVA 和耦合张量分解并行工作来开发问题的有效解决方案来实现的。然后，在第二阶段，在方法和独特性条件方面建立这两种方法之间的联系，以开发一种利用两种方法优势的方法。对解决方案的独特性和可解释性的强调以及对具有挑战性的数据集的应用将确保这些方法以及所开发的理论基础不仅完整而且实用。这项工作的另一个重要方面是在两个不一定沟通的社区之间建立桥梁。这项工作将证明统计和代数驱动的数据融合方法并不相互竞争，而是具有可以有效利用的重要互补方面。此外，清楚地了解它们的联系和差异可以对所有方法进行公平比较，清楚地突出它们的能力及其局限性。这将有助于为不断发展的数据科学和机器学习领域建立坚实且均衡的基础。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。