BIGDATA: Collaborative Research: F: IA: Statistical Learning for Big Data with Random Projections

BIGDATA：协作研究：F：IA：随机投影大数据的统计学习

• 批准号: 1546087
• 负责人: Cheng Yong Tang
• 金额: $ 16.12万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546087&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; IA; Statistical

Contemporary data-driven science and engineering problems require the development of statistical methods that do not compromise statistical accuracy, yet are computationally feasible. Data quality, particularly the heterogeneity in data measurements, is a critical factor that affects statistical accuracy in the analysis of large datasets. This project will explore and demonstrate the impact and feasibility of improving computational and statistical performances simultaneously for Big Data problems with massive datasets. The research will advance the state of knowledge in predictive statistical learning with Big Data, and be extremely valuable in applications related to financial risk management or commercial operations employing recommender systems, biology, and image analysis. A key phenomenon motivating this project is the notion that some refined ensemble methods combined with random projections can simultaneously enable the fast analysis of massive data while enhancing statistical performance. Specifically, the aims of the project are: (1) Develop new classification methods based on random projections and the random forest. By defining appropriate projections, the proposed method is shown to improve statistical accuracy for massive datasets with a large number of irrelevant noisy measurements. The theoretical properties of this method will be analyzed, and an adaptive version of the algorithm developed to optimize the computational and statistical efficiency gains; (2) Propose boosting algorithms with random projections. The statistical properties, practical performance, and implementation of the proposed random projected boosting algorithms will be investigated; (3) Develop classification methods with heterogeneities. A classification method that involves the weighted bootstrap and ensemble learning to handle heterogeneity or covariate shifts in measurements in large datasets will be developed. The random projection method will be applied to improve the proposed method for high-dimensional datasets.

当代数据驱动的科学和工程问题需要开发既不影响统计准确性又在计算上可行的统计方法。 数据质量，特别是数据测量的异质性，是影响大型数据集分析统计准确性的关键因素。 该项目将探索并展示针对海量数据集的大数据问题同时提高计算和统计性能的影响和可行性。 该研究将推进大数据预测统计学习的知识水平，并且在与金融风险管理或采用推荐系统、生物学和图像分析的商业运营相关的应用中非常有价值。 推动该项目的一个关键现象是一些改进的集成方法与随机投影相结合可以同时实现海量数据的快速分析，同时提高统计性能。 具体来说，该项目的目标是：（1）开发基于随机投影和随机森林的新分类方法。 通过定义适当的投影，所提出的方法可以提高具有大量不相关噪声测量的海量数据集的统计准确性。 将分析该方法的理论特性，并开发该算法的自适应版本以优化计算和统计效率增益； (2) 提出具有随机投影的增强算法。 将研究所提出的随机投影增强算法的统计特性、实际性能和实现； (3)开发具有异质性的分类方法。 将开发一种分类方法，涉及加权引导和集成学习，以处理大型数据集中测量的异质性或协变量变化。 将应用随机投影方法来改进针对高维数据集所提出的方法。