EAGER: Hierarchical Topic Modeling by Nonnegative Matrix Factorization for Interactive Multi-scale Analysis of Text Data

EAGER：通过非负矩阵分解进行分层主题建模，用于文本数据的交互式多尺度分析

• 批准号: 1348152
• 负责人: Haesun Park
• 金额: $ 17.5万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1348152&HistoricalAwards=false
• 关键词: EAGER; Hierarchical; Topic; Modeling; Nonnegative

EAGER: Hierarchical Topic Modeling by Nonnegative Matrix Factorization for Interactive Multi-scale Analysis of Text DataNonnegative matrix factorization (NMF) has proven to be an important tool of choice for numerous data analytic problems in text, imaging, and computer vision. It provides advanced mathematical methods for improvements in dimensionality reduction, clustering, etc. A distinguishing feature of the NMF is the requirement of non-negativity in the factors that represent the matrix in a lower rank. This property greatly enhances the interpretability and modeling capability for many applications, where preserving non-negativity is important. This project is studying foundational properties of the NMF, producing new algorithmic methods using the framework of NMF for efficient and effective hierarchical clustering and topic modeling of large scale text data for multi-scale analysis, generating labels for the topics, and interactive analysis. In addition, an interactive visual analytic system for the proposed methods is being developed to make these theoretical and algorithmic discoveries readily available to the research and applications communities. New multi-scale hierarchical methods for generating clusters and discovering topics in the documents and detection of topic changes over time are being explored to enable computationally efficient and perceptually effective ways of exploring text data and discovering latent group structure. Visual analytic systems are also being developed based on this foundational work to enable more effective and informed discovery of topics in a large-scale document collection.This project will have a significant impact on the analysis and development of NMF algorithms and new modeling of problems for applications utilizing the NMF (e.g., 'Big Data'). The project is yielding effective computational methods with solid analysis that will enhance the analysis of high-dimensional data in broad areas of science, engineering, medicine, and business disciplines beyond the application areas being considered within this project.

非负矩阵分解（NMF）已被证明是文本、图像和计算机视觉中许多数据分析问题的重要选择工具。它为降维、聚类等方面的改进提供了先进的数学方法。NMF的一个显著特征是要求在表示较低秩矩阵的因子中非负性。这个属性极大地增强了许多应用程序的可解释性和建模能力，在这些应用程序中，保持非负性是很重要的。该项目正在研究NMF的基本属性，利用NMF框架产生新的算法方法，用于高效和有效的分层聚类和用于多尺度分析的大规模文本数据的主题建模，为主题生成标签和交互式分析。此外，正在为所提出的方法开发一个交互式可视化分析系统，以使这些理论和算法发现随时可供研究和应用社区使用。研究人员正在探索新的多尺度分层方法，用于在文档中生成聚类和发现主题，以及检测主题随时间的变化，以实现高效的计算和感知方式来探索文本数据和发现潜在的组结构。视觉分析系统也正在基于这一基础工作开发，以便在大规模文档集合中更有效和更明智地发现主题。该项目将对NMF算法的分析和开发以及利用NMF应用（例如“大数据”）的新问题建模产生重大影响。该项目正在产生具有可靠分析的有效计算方法，这将增强对科学、工程、医学和商业学科广泛领域的高维数据的分析，超出了本项目所考虑的应用领域。