Graphical Approaches to Modeling High-Dimensional Data

高维数据建模的图形方法

• 批准号: 1219234
• 负责人: Animashree Anandkumar
• 金额: $ 29.41万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1219234&HistoricalAwards=false
• 关键词: Graphical; Approaches; Modeling; Dimensional; Data

This research involves theoretical and applied research on learning and representation of high-dimensional data. The term high dimensionality refers to the property that the number of variablesor ?unknowns? is typically much larger than the number of observations available at hand. A keychallenge is being able to represent and learn such phenomena with sample and computationalrequirements scaling favorably in the number of dimensions. This project addresses these challengesthrough a graphical approach by exploiting the inherent graphical structure present in many largedata-sets.This research considers modeling high-dimensional data through probabilistic graphical models,also known as Markov random fields. An important research thrust of this proposal is to developnovel algorithms for learning and inference under the framework of graphical models. Anotherimportant thrust of this proposal is to develop efficient scalable models for representing high-dimensional data beyond the traditional framework of graphical models. This research establishesstrong theoretical guarantees for the developed methods, as well as applies them to real data invarious domains, including genetic and financial data, and data from large online social networkssuch as Facebook and Twitter.

这项研究涉及有关高维数据学习和表示的理论和应用研究。术语高维度是指变量器数量未知数的属性？通常比目前可用的观测值大得多。钥匙琴是能够用样本和计算要求在维度数量上缩放的样本和计算要求的现象。该项目通过利用许多LargedAta-Sets中存在的固有图形结构来解决这些挑战，这是通过概率图形模型（也称为Markov随机字段）对高维数据进行建模的。该提案的一个重要研究作用是在图形模型的框架下开发用于学习和推断的算法。该提案的另一个重要的目的是开发有效的可扩展模型，用于代表除传统图形模型框架之外的高维数据。这项研究为开发方法建立了理论保证，并将其应用于包括遗传和财务数据在内的真实数据不变的领域，以及来自大型在线社交网络的数据，例如Facebook和Twitter。