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.

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非负矩阵分解（NMF）已被证明是文本、成像和计算机视觉中众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 的一个显着特征是要求代表较低阶矩阵的因子非负性。 此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，保持非负性非常重要。该项目正在研究 NMF 的基本特性，使用 NMF 框架产生新的算法方法，用于高效且有效的大规模文本数据的层次聚类和主题建模，以进行多尺度分析、为主题生成标签和交互式分析。 此外，正在开发针对所提出方法的交互式视觉分析系统，以使这些理论和算法发现易于研究和应用社区使用。正在探索用于生成聚类和发现文档中的主题以及检测主题随时间变化的新的多尺度分层方法，以实现探索文本数据和发现潜在组结构的计算高效和感知有效的方法。视觉分析系统也基于这项基础工作而开发，以便能够更有效、更明智地发现大规模文档集中的主题。该项目将对 NMF 算法的分析和开发以及新的问题建模产生重大影响。利用 NMF 的应用程序（例如“大数据”）。 该项目正在产生有效的计算方法和可靠的分析，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，超出了该项目所考虑的应用领域。