CAREER: Learning from Coarse, Nonmetric, and Incomplete Data

职业：从粗略、非度量和不完整的数据中学习

• 批准号: 1350616
• 负责人: Mark Davenport
• 金额: $ 47.47万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-15 至 2020-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1350616&HistoricalAwards=false
• 关键词: CAREER; Learning; Coarse; Nonmetric; Incomplete

In recent years, we have witnessed an explosion in the amounts of data being acquired and analyzed in a wide variety of contexts. Data-driven techniques are increasingly applied, not only in the traditional quantitative sciences, but also throughout the social sciences and in a variety of other non-traditional scenarios that challenge many of our common assumptions. For example, in contexts such as collaborative filtering, personalized and predictive medicine, and personalized learning systems, we face a variety of challenges due largely to the fact that an important -- often the only -- source of data is people. In these and many other modern applications, we want to learn about people using the data that people supply.This presents several difficulties, including the fact that such data is often very "coarse" or heavily "quantized". It might even be binary or entirely nonmetric data consisting of categories or comparisons. Moreover, in many of these cases it is impossible to fully sample the data, and the underlying data of interest may be constantly changing, necessitating approaches that can handle incomplete observations and dynamic data models. This research confronts these difficulties by building on recent progress in the design of efficient algorithms for exploiting low-dimensional structure to perform inference, often using highly incomplete and coarse observations. This research addresses a number of fundamental theoretical and algorithmic questions in the context of low-rank matrix recovery, nonmetric multidimensional scaling, unfolding, and low-dimensional dynamic models. It has applications in contexts such as collaborative filtering, personalized and predictive medicine, and personalized learning systems.

近年来，我们目睹了在各种背景下获取和分析的数据量的爆炸式增长。数据驱动技术的应用越来越多，不仅在传统的定量科学中，而且在整个社会科学和各种其他非传统场景中，这些场景挑战了我们的许多常见假设。例如，在协同过滤、个性化和预测医学以及个性化学习系统等背景下，我们面临着各种挑战，这主要是因为一个重要的（通常是唯一的）数据来源是人。在这些和许多其他现代应用中，我们希望了解使用人们提供的数据的人，这带来了一些困难，包括这些数据通常非常“粗糙”或严重“量化”的事实。它甚至可能是由类别或比较组成的二进制或完全非度量数据。此外，在许多情况下，不可能对数据进行全面抽样，而且相关的基本数据可能不断变化，因此需要采取能够处理不完整观测和动态数据模型的方法。本研究面临这些困难的基础上，利用低维结构进行推理，往往使用高度不完整和粗糙的观察，在设计有效的算法的最新进展。 本研究解决了一些基本的理论和算法问题的背景下，低秩矩阵恢复，非度量多维缩放，展开，低维动态模型。它在协同过滤、个性化和预测医学以及个性化学习系统等方面都有应用。