高维数据的近似最近邻搜索-实验、分析与改进（v1.0）
Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)
摘要：
近似最近邻搜索是数据库、机器学习、多媒体、计算机视觉等领域应用中必不可少的基本操作。虽然每年都有许多算法被不断地提出，但并没有对它们的性能进行全面的评价和分析。在本文中，我们进行了全面的实验评估的许多国家的最先进的方法近似最近邻搜索。我们的研究（1）是跨学科的（即，包括不同领域中的16种算法，并且来自实践者）和（2）已经评估了各种各样的设置，包括20个数据集、若干评估度量和不同的查询工作负载。实验结果仔细报告和分析，以了解性能结果。此外，我们提出了一种新的方法，实现了高查询效率和高召回经验上的大多数数据集在广泛的设置。
英文摘要：
Approximate Nearest neighbor search (ANNS) is fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision. Although many algorithms have been continuously proposed in the literature in the above domains each year, there is no comprehensive evaluation and analysis of their performances. In this paper, we conduct a comprehensive experimental evaluation of many state-of-the-art methods for approximate nearest neighbor search. Our study (1) is cross-disciplinary (i.e., including 16 algorithms in different domains, and from practitioners) and (2) has evaluated a diverse range of settings, including 20 datasets, several evaluation metrics, and different query workloads. The experimental results are carefully reported and analyzed to understand the performance results. Furthermore, we propose a new method that achieves both high query efficiency and high recall empirically on majority of the datasets under a wide range of settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
