EAGER: Collaborative Research: Cross-Domain Knowledge Transformation via Matrix Decompositions

EAGER：协作研究：通过矩阵分解进行跨领域知识转换

• 批准号: 0939187
• 负责人: Chris Ding
• 金额: $ 5.39万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0939187&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Cross; Domain

EAGER: Collaborative Research: Cross-domain Knowledge Transformation via Matrix DecompositionsTraditional data mining algorithms discover knowledge in new domains starting from the scratch, ignoring knowledge learned in other domains. Knowledge transformation is a transformative paradigm that utilizes previously acquired knowledge in other domains to guide knowledge discovery process in a new domain and is especially useful for large data sets. In particular, utilizing applicable knowledge in other domains helps to stabilize the unsupervised learning and generate results that we may have preliminary understanding. The goal of this project is to design and develop cross-domain knowledge transformation mechanisms for knowledge discovery. The transformation mechanisms are based on matrix decompositions where the knowledge been transferred are represented directly and explicitly ? making them easy to comprehend and be utilized in practice. The proposed mechanisms provide a versatile knowledge transformation framework with solid theoretical foundation and enable a new paradigm of unsupervised learning with domain knowledge. The usefulness of these knowledge transformation mechanisms/systems will be demonstrated for effective information retrieval, consumer recommender systems, and product/online opinion sentiment analysis. The versatility of this transformative metholody will be verified across many domains.

EAGER：协作研究：通过矩阵分解进行跨领域知识转换传统的数据挖掘算法从头开始发现新领域的知识，而忽略了在其他领域学到的知识。 知识转换是一种变革范式，利用先前在其他领域获得的知识来指导新领域的知识发现过程，对于大型数据集特别有用。 特别是，利用其他领域的适用知识有助于稳定无监督学习并产生我们可能初步理解的结果。 该项目的目标是设计和开发用于知识发现的跨领域知识转换机制。转换机制基于矩阵分解，其中所转移的知识被直接且明确地表示 ?使它们易于理解并在实践中使用。所提出的机制提供了具有坚实理论基础的多功能知识转换框架，并实现了领域知识无监督学习的新范式。这些知识转换机制/系统的有用性将在有效的信息检索、消费者推荐系统和产品/在线意见情感分析中得到证明。这种变革方法的多功能性将在许多领域得到验证。