EAGER: Preliminary Study of Hashing Algorithms for Large-Scale Learning

EAGER：大规模学习的哈希算法初步研究

• 批准号: 1249316
• 负责人: Ping Li
• 金额: $ 10万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1249316&HistoricalAwards=false
• 关键词: EAGER; Preliminary; Study; Hashing; Algorithms

Many emerging applications of data mining call for techniques that can deal with data instances with millions, if not billions of dimensions. Hence, there is a need for effective approaches to dealing with extremely high dimensional data sets. This project focuses on a class of novel theoretically well-founded hashing algorithms that allow high dimensional data to be encoded in a form that can be efficiently processed by standard machine learning algorithms. Specifically, it explores: One-permutation hashing, to dramatically reduce the computational and energy cost of hashing; Sparsity-preserving hashing, to take advantage of data sparsity for efficient data storage and improved generalization; Application of the new hashing techniques with standard algorithms for learning "linear" separators in high dimensional spaces. The success of this EAGER project could lay the foundations of a longer-term research agenda by the PI and other investigators focused on developing effective methods for building predictive models from extremely high dimensional data using "standard" machine learning algorithms. Broader Impacts: Effective approaches to building predictive models from extremely high dimensional data can impact many areas of science that rely on machine learning as the primary methodology for knowledge acquisition from data. The PI's education and outreach efforts aim to broaden the participation of women and underrepresented groups. The publications, software, and datasets resulting from the project will be freely disseminated to the larger scientific community.

许多新兴的数据挖掘应用需要能够处理数百万甚至数十亿维数据实例的技术。因此，需要有效的方法来处理极高维数据集。该项目专注于一类新的理论上有充分依据的哈希算法，允许高维数据以标准机器学习算法可以有效处理的形式进行编码。具体而言，它探讨了：单排列散列，以显着降低散列的计算和能源成本;稀疏性保持散列，利用数据稀疏性进行有效的数据存储和改进的泛化;新的散列技术与标准算法的应用，用于学习高维空间中的“线性”分隔符。这个EAGER项目的成功可以为PI和其他研究人员的长期研究议程奠定基础，这些研究人员专注于开发使用“标准”机器学习算法从极高维数据构建预测模型的有效方法。更广泛的影响：从极高维数据中构建预测模型的有效方法可以影响许多依赖机器学习作为从数据中获取知识的主要方法的科学领域。PI的教育和外联工作旨在扩大妇女和代表性不足群体的参与。该项目产生的出版物、软件和数据集将免费传播给更广泛的科学界。