CAREER: Statistical Methodology in Multi-view Learning with Large Data

职业：大数据多视图学习的统计方法

• 批准号: 1255045
• 负责人: Mark Culp
• 金额: $ 40万
• 依托单位: West Virginia University 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: 2013-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1255045&HistoricalAwards=false
• 关键词: CAREER; Statistical; Methodology; Multi; view

In complex scientific research classification and regression problems, it is common for several different data sets to be used to describe the response. Each of these data sets provides a unique view of the response, but typically none of these views describes the response perfectly. The investigator develops computationally efficient statistical methodology to model multi-view data within a framework for statistical analysis, variable and view selection, and the interpretation of results. The methodology is based on both regularization approaches involving sparse penalties in additive models and algorithmic intensive iterative-based approaches. In addition, the investigator provides a solid foundation for analysis of residuals in this context, establishes the consistency of the model, and addresses the practical issue of concurvity. The investigator is committed to raising awareness in scientific research communities and industries of the advantages of using these modeling techniques in the analysis of complex research problems involving data from multiple sources and to training future statisticians and related professionals through hands-on experiences with these data sets. To support this effort, the investigator develops and maintains a powerful, user-friendly statistical software package to implement this methodology.In the modern world our ability to collect data from many different sources has expanded dramatically due in part to computer innovations over the past few decades. What has not kept pace is the ability to analyze data from many different sources simultaneously. As a result, scientific researchers in academia and industry are not fully harnessing the information that can be found by appropriately combining multiple, diverse sources of data in a way that can provide interpretable results. This is a challenging problem involving advances in statistics, computer science, mathematics, and database management. The investigator addresses this problem from a statistical analysis viewpoint. Many applications of this research involve new statistical methods to help with cancer research, pharmacology, genetics, proteomics, text data processing, and homeland security. The need for analyses of large, complex, multi-view data sets is substantial in scientific research today, and this need is currently unmet. The results of this work are transforming how researchers in many fields analyze and interpret data.

在复杂的科学研究分类和回归问题中，通常使用几个不同的数据集来描述响应。这些数据集中的每一个都提供了响应的唯一视图，但通常这些视图都不能完美地描述响应。 研究人员开发计算效率高的统计方法，在统计分析，变量和视图选择以及结果解释的框架内对多视图数据进行建模。 该方法是基于两个正则化方法，涉及稀疏的罚款添加剂模型和算法密集的迭代为基础的方法。 此外，研究者提供了一个坚实的基础，在这种情况下，残差分析，建立模型的一致性，并解决并发性的实际问题。该研究员致力于提高科学研究界和行业对使用这些建模技术分析涉及多个来源数据的复杂研究问题的优势的认识，并通过这些数据集的实践经验培训未来的统计学家和相关专业人员。 为了支持这一努力，调查员开发和维护一个强大的，用户友好的统计软件包，以实现这一方法。在现代世界中，我们从许多不同的来源收集数据的能力已经急剧扩大，部分原因是在过去几十年的计算机创新。 没有跟上步伐的是同时分析来自许多不同来源的数据的能力。 因此，学术界和工业界的科学研究人员没有充分利用通过以一种可以提供可解释结果的方式适当组合多种多样的数据来源可以找到的信息。这是一个具有挑战性的问题，涉及统计学，计算机科学，数学和数据库管理的进步。 研究者从统计分析的角度来解决这个问题。 这项研究的许多应用涉及新的统计方法，以帮助癌症研究，药理学，遗传学，蛋白质组学，文本数据处理和国土安全。 在当今的科学研究中，对大型、复杂、多视图数据集的分析的需求是巨大的，而这种需求目前尚未得到满足。这项工作的结果正在改变许多领域的研究人员分析和解释数据的方式。