Collaborative Research: Joint Analysis of Correlated Data

合作研究：相关数据的联合分析

• 批准号: 1521608
• 负责人: Leonidas Guibas
• 金额: $ 14万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1521608&HistoricalAwards=false
• 关键词: Collaborative; Research; Joint; Analysis; Correlated

Across science, engineering, medicine and business we face a deluge of data coming from sensors, from simulations, or from the activities of myriads of individuals on the Internet. Furthermore, the data sets we collect are frequently highly inter-correlated, reflecting information about the same or similar/related entities in the world, or echoing semantically important repetitions/symmetries or hierarchical structures common to both man-made and natural objects. This project will assist scientists and engineers working with correlated data sets in getting the most information and value out of their data. Key to the approach is the idea of joint data analysis, the notion that each piece of data is best understood not in isolation but in the context provided by its peers and partners in a collection of related data sets, using the web of relationships referred to above. The key aim is to complement the social networks of scientists and engineers as they exist today with parallel networks that interlink the data they base their work on, using domain-specific semantic links and aiming at mechanisms that allow algorithmic transport of information between data used by scientists working in the same domain. The resulting system amplifies scientific insights by allowing an observation of one scientist on one piece of data to automatically be transported to other relevant data sets and aggregated and also enables the automated discovery of shared structures or common abstractions that can inform multiple data sets.In order to accomplish this joint analysis this project interconnects data sets into networks along which information can be transported and aggregated. These data set links are based on efficient matching algorithms using domain-specific features. In the associated setting, these matching or maps are used not to estimate distances or similarities but to build operators that can transport information between different data sets. The research team will exploit a functional analytic framework that allows for encoding of information as functions over the data and leads to linear operators for mapping, enabling the use of many powerful tools from linear algebra and optimization. Using inspiration from homological algebra, this team will join multiple related data sets into networks connected through these operators in a way that allows information transport, correction, and aggregation, with the ultimate goal of using the "wisdom of the collection" to provide as much information as possible for specific data sets to specific scientists.

在科学、工程、医学和商业领域，我们面临着来自传感器、模拟或互联网上无数个人活动的海量数据。此外，我们收集的数据集往往是高度相互关联的，反映了世界上相同或相似/相关实体的信息，或者反映了人工和自然物体共同的语义上重要的重复/对称或层次结构。该项目将帮助研究相关数据集的科学家和工程师从数据中获得最多的信息和价值。这种方法的关键是联合数据分析的思想，即最好不是孤立地理解每一项数据，而是利用上述关系网，在相关数据集集合中的同类和合作伙伴提供的背景下理解每一项数据。关键目标是补充科学家和工程师的社会网络，因为他们今天存在的并行网络，相互连接的数据，他们的工作，使用特定领域的语义链接，并针对机制，允许算法之间的信息传输的科学家使用的数据在同一领域工作。由此产生的系统通过允许将一位科学家对一段数据的观察自动传输到其他相关数据集并进行汇总，从而增强了科学见解，并且还可以自动发现可以为多个数据集提供信息的共享结构或公共抽象。为了完成这种联合分析，该项目将数据集连接到网络中，从而可以传输和汇总信息。这些数据集链接基于使用特定领域特征的高效匹配算法。在相关设置中，这些匹配或映射不是用来估计距离或相似性，而是用来构建可以在不同数据集之间传输信息的运算符。研究团队将开发一个功能分析框架，该框架允许将信息编码为数据上的函数，并导致用于映射的线性算子，从而能够使用线性代数和优化中的许多强大工具。利用同态代数的灵感，该团队将多个相关的数据集连接到网络中，通过这些算子以允许信息传输、校正和聚合的方式连接，最终目标是使用“集合的智慧”为特定的数据集提供尽可能多的信息给特定的科学家。

项目成果

Deep Part Induction from Articulated Object Pairs

• DOI: 10.1145/3272127.3275027
• 发表时间: 2018-11-01
• 期刊: ACM TRANSACTIONS ON GRAPHICS
• 影响因子: 6.2
• 作者: Yi, Li;Huang, Haibin;Guibas, Leonidas
• 通讯作者: Guibas, Leonidas