CRII: III: Explainable Multi-Source Data Integration with Uncertainty

我们周围到处都是收集数据的传感器。每个传感器可以提供支持诸如目标检测、分类或场景理解等任务的补充或增强信息。例如，在遥感应用中，高光谱成像传感器可以通过使用广泛的波长范围来提供有关材料的光谱信息，而激光雷达（光探测和测距）则测量物体离地面的高度。如果道路和建筑物的屋顶是用相同的材料（例如沥青）建造的，那么单独的高光谱信息可能不足以区分它们，而整合激光雷达数据的高度信息则可以更容易地区分两者。该项目将开发创新的数学框架和相关算法，用于整合来自多个来源的此类传感器数据。该项目的主要新颖之处在于它能够在数据集成过程中学习多个源之间的关系和非线性相互作用，同时解决现实世界传感器数据中常见的数据和标签不确定性。这项工作将推进可解释数据集成的基础知识，并适用于具有重大社会影响的广泛数据密集型应用，包括遥感，自动驾驶，机器人视觉和感知以及精准农业。这项工作还将支持跨学科研究和教育活动，包括指导来自不同背景的学生，通过实践设计项目开展外展活动，以及在密歇根大学开发本科生和研究生水平的数据集成和机器学习课程。该项目旨在通过为异构多传感器数据集成开发一个统一的、弱监督的基于学习的框架来弥合现有的知识差距，同时考虑到数据和标签的不确定性。本项目涉及以下三个研究组成部分。本研究的第一个目标是在多实例学习框架下开发一种可扩展且高效的计算模型，该模型利用二元模糊度量，将减少非线性聚合算子（如Choquet积分（CI））的搜索空间，并在处理大量数据源的数据集成时显着提高鲁棒性。第二个目标是利用双能力和双极性CI来解释数据源中证据的冲突和否定。将进行实验来研究每个数据源及其组合如何对不同的集成结果做出贡献。当整合复杂和异构数据集时，本研究将允许更大的可解释性和适应性。第三个目标是扩展当前用于顺序输入的多实例CI框架，例如多视图和多时间数据，这将揭示使用CI进行时空数据集成的未开发潜力。本研究将使数据集成具有可管理性、可解释性，并适用于大量的多模态数据，以促进决策和知识发现。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Sensors are all around us collecting data. Each sensor may provide complementary or reinforced information that supports tasks such as target detection, classification, or scene understanding. In remote sensing applications, for example, hyperspectral imaging sensors can provide spectral information about materials by using a wide range of wavelengths, while LiDAR (light detection and ranging) measures an object's elevation above the ground. If a road and a building rooftop are built with the same material (e.g., asphalt), hyperspectral information alone may not be sufficient to tell them apart, while integrating height information from LiDAR data makes it easier to distinguish the two. This project will develop innovative mathematical framework and associated algorithms for integrating such sensor data from multiple sources. The main novelty of this project will be in its capacity to learn the relationships and non-linear interactions among multiple sources during data integration, while addressing data and label uncertainties commonly observed in real-world sensor data. This work will advance the fundamental knowledge in explainable data integration and is applicable to a broad range of data-intensive applications with significant social impact including remote sensing, autonomous driving, robotic vision and perception, and precision agriculture. This work will also support cross-disciplinary research and educational activities, including mentoring students from diverse backgrounds, outreach activities through hands-on design projects and the development of undergraduate and graduate-level courses on data integration and machine learning at the University of Michigan.This project aims to bridge existing gaps in knowledge by developing a unified, weakly supervised learning-based framework for heterogeneous multi-sensor data integration, accounting for both data and label uncertainties. This project addresses the following three research components. The first objective of this research is to develop a scalable and efficient computational model leveraging the binary fuzzy measures under the multiple instance learning framework, which will reduce the search space of the non-linear aggregation operator such as the Choquet integral (CI) and be significantly more robust at handling data integration for larger numbers of sources. The second objective is to leverage bi-capacities and bi-polar CI to account for conflicts and negation of evidence in data sources. Experiments will be conducted to investigate how each data source and their combinations contribute to different integration outcomes. This study will allow for greater explainability and adaptability when integrating complex and heterogeneous datasets. The third objective is to extend the current multiple instance CI framework for sequential inputs, such as multi-view and multi-temporal data, which will uncover the untapped potential of using CI for spatiotemporal data integration. This research will make data integration manageable, interpretable, and applicable with large volumes of multi-modal data to facilitate decision making and knowledge discovery.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.