CAREER: Undirected Bipartite Graphical Models

职业：无向二分图模型

• 批准号: 0447903
• 负责人: Max Welling
• 金额: $ 45万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-03-15 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0447903&HistoricalAwards=false
• 关键词: CAREER; Undirected; Bipartite; Graphical; Models

Modern society increasingly relies on processing, storing and communicating large amounts of information. The exponential growth of databases necessitates the development of algorithms that structure, compress and query them efficiently. The goal of this research is to develop new tools based on probabilistic models to achieve these objectives.A new class of "undirected bipartite graphical models" is studied that embeds documents into a low dimensional "topic-space". These representations are efficient and capture semantic relationships. Training of the underlying probabilistic model is achieved through a technique called "contrastive divergence learning" which is particularly well adapted to the UBG model. The main contributions of this research are the development of a new class of probabilistic graphical model, the development of improved learning algorithms that scale up to large data-sets and the application of these novel techniques to two real world applications: image restoration and information retrieval.Research is integrated with teaching through the development of new classes in machine learning both at the undergraduate and the graduate level, where students will be engaged in research in the above application areas.The proposed research makes important contributions that can have a broad impact on security, web-technologies, commerce, multi-media, medical expert systems etc. In particular, the proposed projects in image restoration and information retrieval have the potential to make an impact on tomorrow's technologies. An open-source, web-based repository with freely available software will be developed to help achieve that goal.http://www.ics.uci.edu/~welling/NSFcareer/NSFcareer.html

现代社会越来越依赖于处理、存储和交流大量信息。数据库的指数级增长要求开发高效地组织、压缩和查询数据库的算法。本研究的目标是开发基于概率模型的新工具来实现这些目标。研究了一类新的将文档嵌入到低维主题空间中的无向二部图模型。这些表示是有效的，并且捕获了语义关系。对基本概率模型的训练是通过一种称为“对比发散学习”的技术来实现的，这种技术特别适合于UBG模型。这项研究的主要贡献是开发了一类新的概率图形模型，开发了可扩展到大数据集的改进的学习算法，并将这些新技术应用于两个现实世界的应用：图像恢复和信息检索。研究与教学相结合，通过在本科生和研究生水平上开发机器学习的新课程，学生将从事上述应用领域的研究。所提出的研究做出了重要贡献，可以对安全、网络技术、商业、多媒体、医学专家系统等产生广泛的影响。特别是，拟议的图像恢复和信息检索项目有可能对未来的技术产生影响。将开发一个开放源码的基于Web的存储库，并提供免费的软件，以帮助实现这一goal.http://www.ics.uci.edu/~welling/NSFcareer/NSFcareer.html