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.

渴望：协作研究：通过矩阵分解跨度数据挖掘算法通过scratch中发现知识的跨域知识转换，从而忽略了在其他域中学到的知识。 知识转换是一种变革性的范式，它利用先前在其他领域中获得的知识来指导新领域中的知识发现过程，对于大型数据集尤其有用。 特别是，在其他领域中利用适用的知识有助于稳定无监督的学习，并产生我们可能具有初步理解的结果。 该项目的目的是设计和开发用于知识发现的跨域知识转换机制。转换机制基于矩阵分解，在该矩阵分解中，传递知识的传递直接和明确表示？使它们易于理解并在实践中使用。提出的机制提供了一个具有稳固理论基础的多功能知识转换框架，并可以通过领域知识实现无监督学习的新范式。这些知识转换机制/系统的有用性将被证明，以进行有效的信息检索，消费者推荐系统以及产品/在线意见情绪分析。该变革性甲基疾病的多功能性将在许多领域进行验证。