High Dimensional Statistical Theory for Sparse Regularization

稀疏正则化的高维统计理论

• 批准号: 1007527
• 负责人: Tong Zhang
• 金额: $ 25万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1007527&HistoricalAwards=false
• 关键词: Dimensional; Statistical; Theory; Sparse; Regularization

High Dimensional Statistical Theory for Sparse RegularizationThe investigator studies statistical machine learning with sparse regularization in the setting of high dimensional statistical estimation. A number of research directions will be explored, including improved performance bounds for sparse regularization, new sparse learning formulations, and the statistical theory for several important computational algorithms.In the information age, more and more data become available electronically, and these data need to be automatically analyzed by computers in order to filter out the most important information. Statistical machine learning is the main technical tool for analyzing electronic data. Many modern applications involve data in very high dimension that cannot be handled by traditional algorithms. Sparse regularization is an important new statistical machine learning technique that can deal with this issue by effectively identifying the most significant patterns from a vast amount of available information. This research develops new sparse regularization algorithms that will significantly enhance the capability for modern computer systems to find critical information from available electronic data.

稀疏正则化的高维统计理论研究者在高维统计估计的背景下研究具有稀疏正则化的统计机器学习。本课程将探讨多个研究方向，包括稀疏正则化的改进性能边界、新的稀疏学习公式以及几种重要计算算法的统计理论。在信息时代，越来越多的数据以电子方式获得，这些数据需要计算机自动分析，以筛选出最重要的信息。统计机器学习是分析电子数据的主要技术工具。许多现代应用程序涉及到传统算法无法处理的高维数据。稀疏正则化是一种重要的新统计机器学习技术，可以通过从大量可用信息中有效识别最重要的模式来处理这个问题。这项研究开发了新的稀疏正则化算法，这将显着提高现代计算机系统从可用电子数据中找到关键信息的能力。