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: 1545994
• 负责人: Wen Zhou
• 金额: $ 10.06万
• 依托单位: Colorado State 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 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1545994&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不损害统计准确性的统计方法，但在计算上是可行的。 数据质量，尤其是数据测量中的异质性，是影响大型数据集分析的统计准确性的关键因素。 该项目将探索并证明与大量数据集的大数据问题同时改善计算和统计性能的影响和可行性。 这项研究将通过大数据提高预测性统计学习的知识状态，并在使用推荐系统，生物学和图像分析的财务风险管理或商业操作有关的应用中非常有价值。 激发该项目的关键现象是，某些精制的集合方法与随机预测结合可以同时实现大规模数据的快速分析，同时增强统计性能。 具体而言，该项目的目的是：（1）根据随机预测和随机森林开发新的分类方法。 通过定义适当的预测，提出的方法被证明可以提高大量无关的嘈杂测量值的大量数据集的统计准确性。 将分析该方法的理论属性，并开发出算法的自适应版本，以优化计算和统计效率提高； （2）提出具有随机投影的增强算法。 将研究提出的随机预测促进算法的统计属性，实际绩效和实施； （3）开发具有异质性的分类方法。 将开发一种涉及加权引导和集合学习以处理异质性或协变量转移的分类方法。 随机投影方法将应用于改进高维数据集的建议方法。