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

许多新兴的数据挖掘应用程序需要能够处理具有数百万甚至数十亿维度的数据实例的技术。因此，需要一种有效的方法来处理极高维度的数据集。该项目专注于一类理论上有充分根据的新颖哈希算法，该算法允许将高维数据编码为可由标准机器学习算法有效处理的形式。具体来说，它探讨了：单排列哈希，以显着减少哈希的计算和能源成本；保持稀疏性哈希，利用数据稀疏性实现高效的数据存储和改进的泛化；应用新的哈希技术和标准算法来学习高维空间中的“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，这些研究重点是开发有效的方法，使用“标准”机器学习算法从极高维度的数据中构建预测模型。更广泛的影响：从极高维度的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大的科学界。

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.