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

EAGER：基于非负矩阵分解的文本数据交互式多尺度分析分层主题建模非负矩阵分解（NMF）已被证明是文本、图像和计算机视觉中众多数据分析问题的重要工具。它提供了先进的数学方法，用于改进降维，聚类等的NMF的一个显着特点是要求在一个较低的秩表示矩阵的因素的非负性。 这个属性大大增强了许多应用程序的可解释性和建模能力，其中保持非负性很重要。该项目正在研究NMF的基本属性，使用NMF的框架产生新的算法方法，用于大规模文本数据的高效和有效的层次聚类和主题建模，用于多尺度分析，为主题生成标签，以及交互式分析。 此外，一个互动的视觉分析系统，所提出的方法正在开发，使这些理论和算法的发现随时提供给研究和应用社区。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的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。可视化分析系统也正在基于这一基础性工作进行开发，以实现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的应用程序的新问题建模产生重大影响（例如，“大数据”）。 该项目正在产生具有坚实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，超出了该项目中考虑的应用领域。

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.