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

传感器就在我们周围收集数据。每个传感器可以提供补充或加强的信息，以支持诸如目标检测、分类或场景理解之类的任务。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来提供关于材料的光谱信息，而LiDAR(光检测和测距)则测量物体离地面的高度。如果道路和建筑物屋顶是用相同的材料(例如沥青)建造的，单靠高光谱信息可能不足以区分它们，而整合来自激光雷达数据的高度信息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，以整合来自多个来源的此类传感器数据。该项目的主要创新之处在于，它能够了解数据整合过程中多个来源之间的关系和非线性相互作用，同时解决在真实世界传感器数据中经常观察到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的研究和教育活动，包括指导来自不同背景的学生，通过动手设计项目进行推广活动，以及在密歇根大学开发关于数据集成和机器学习的本科生和研究生水平的课程。该项目旨在通过开发一个统一的、弱监督的基于学习的框架来弥补现有的知识差距，以实现异质多传感器数据集成，同时考虑到数据和标签的不确定性。该项目涉及以下三个研究部分。该研究的第一个目标是在多实例学习框架下开发一个利用二元模糊度量的可扩展和高效的计算模型，该模型将减少Choket积分(CI)等非线性聚集算子的搜索空间，并且在处理大量源的数据整合时显著地更加健壮。第二个目标是利用双能力和两极信息来解决数据来源中的冲突和证据否定问题。将进行实验，以调查每个数据源及其组合如何对不同的集成结果做出贡献。这项研究将允许在整合复杂和不同种类的数据集时具有更好的解释性和适应性。第三个目标是扩展目前用于顺序输入的多实例CI框架，例如多视图和多时态数据，这将揭示使用CI进行时空数据集成的未开发潜力。这项研究将使数据集成可管理、可解释和适用于大量多模式数据，以促进决策和知识发现。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。

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.