Hidden Components in Modern Applications

现代应用中的隐藏组件

• 批准号: 1712958
• 负责人: Zheng Ke
• 金额: $ 20万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712958&HistoricalAwards=false
• 关键词: Hidden; Components; Modern; Applications

In the era of Big Data, researchers often encounter datasets that are large in size and complex in structure where the information of interest is usually contained in "components" hidden in the enormous amount of noise. Examples include communities in large social networks, topics in text documents, and confounding factors in genome-wide association studies (GWAS). Extracting these hidden components is an interesting but challenging problem. This project will address these challenges and include applications to many scientific areas including social networks, text mining, genomics, and genetics. The project will include (a) collection of large social networks data, (b) development of new models, methods, and theory for extracting hidden components in network analysis, text mining, and genome-wide association studies, and (c) a study of knowledge discovery using academic research data such as co-authorship and citation relationships. The research will have an impact in linguistics, social sciences, cancer research, and knowledge discovery. This project aims to develop statistical models, methods, and theory for inferring and utilizing hidden components in complex data, especially matrix data. The goals of the project include: (1) Development of simple and fast methods for network mixed membership estimation and topic model estimation. These methods, based on nontrivial modifications of Principal Component Analysis (PCA), are easy to implement and can handle very large data. (2) New methods and theory for detecting and estimating rare and weak effects in GWAS. Problems related to optimal ranking of genes in the presence of complex correlation structures and detection of weak spikes in large covariance matrices will be considered. (3) A study of social network structures of scientific researchers. The PI and her collaborators will collect meta-information from published articles in representative statistics journals to understand social network structures and other features of the statistics community. (4) Development of new random matrix theory (RMT) for statistical analysis.

在大数据时代，研究人员经常遇到规模庞大、结构复杂的数据集，其中感兴趣的信息通常包含在隐藏在大量噪声中的“组件”中。 示例包括大型社交网络中的社区、文本文档中的主题以及全基因组关联研究 (GWAS) 中的混杂因素。 提取这些隐藏的组件是一个有趣但具有挑战性的问题。该项目将解决这些挑战，并包括在许多科学领域的应用，包括社交网络、文本挖掘、基因组学和遗传学。该项目将包括（a）收集大型社交网络数据，（b）开发用于提取网络分析、文本挖掘和全基因组关联研究中隐藏成分的新模型、方法和理论，以及（c）一项研究使用学术研究数据（例如合著者和引用关系）进行知识发现。该研究将对语言学、社会科学、癌症研究和知识发现产生影响。 该项目旨在开发统计模型、方法和理论，用于推断和利用复杂数据（尤其是矩阵数据）中的隐藏成分。 该项目的目标包括：（1）开发简单快速的网络混合隶属度估计和主题模型估计方法。这些方法基于主成分分析 (PCA) 的重大修改，易于实现并且可以处理非常大的数据。 (2) GWAS中检测和评估罕见效应和微弱效应的新方法和理论。 将考虑与存在复杂相关结构的情况下基因的最佳排序以及大协方差矩阵中的弱尖峰检测相关的问题。 (3)科研人员社会网络结构研究。 PI 和她的合作者将从代表性统计期刊上发表的文章中收集元信息，以了解社交网络结构和统计界的其他特征。 (4) 发展用于统计分析的新随机矩阵理论(RMT)。

项目成果

Network global testing by counting graphlets

通过计算 graphlet 进行网络全局测试

• 发表时间: 2018-10
• 期刊: Proceedings of Machine Learning Research
• 作者: Jin, Jiashun;Ke, Zheng Tracy;Luo, Shengming
• 通讯作者: Luo, Shengming

Phase transitions for high dimensional clustering and related problems

高维聚类的相变及相关问题

• DOI: 10.1214/16-aos1522
• 发表时间: 2017-10
• 期刊: The Annals of Statistics
• 作者: Jin, Jiashun;Ke, Zheng Tracy;Wang, Wanjie
• 通讯作者: Wang, Wanjie