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.

本研究涉及高维数据学习与表示的理论与应用研究。术语“高维”指的是变量或未知数的数量。通常要比手头的观测数据大得多。一个关键的挑战是能够用样本和计算需求在维数上有利地缩放来表示和学习这些现象。该项目通过利用许多大型数据集中存在的固有图形结构，通过图形方法解决了这些挑战。本研究考虑通过概率图形模型（也称为马尔可夫随机场）对高维数据进行建模。本提案的一个重要研究重点是在图形模型框架下开发新的学习和推理算法。该建议的另一个重要推力是开发有效的可扩展模型，以超越传统的图形模型框架来表示高维数据。本研究为所开发的方法建立了强有力的理论保障，并将其应用于各个领域的真实数据，包括遗传和金融数据，以及来自Facebook和Twitter等大型在线社交网络的数据。