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

许多新兴的数据挖掘应用需要能够处理数百万(如果不是数十亿)维数据实例的技术。因此，需要有效的方法来处理超高维数据集。本项目的重点是一类新颖的理论上有充分依据的哈希算法，它允许高维数据以一种标准机器学习算法可以有效处理的形式进行编码。具体地说，它探索了：单排列散列，以显著降低散列的计算和能量成本；稀疏性保持散列，以利用数据稀疏性来有效地存储数据和改进泛化；新的散列技术与标准算法在高维空间中学习线性分隔符的应用。这个急切的项目的成功可能会为PI和其他研究人员制定更长期的研究议程奠定基础，这些研究人员专注于开发有效的方法，使用“标准”机器学习算法从超高维数据中构建预测模型。更广泛的影响：从超高维数据建立预测模型的有效方法可能会影响依赖机器学习作为从数据获取知识的主要方法的许多科学领域。国际和平协会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分发给更大的科学界。

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.