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Approximate Nearest neighbor search (ANNS) is fundamental and essentialoperation in applications from many domains, such as databases, machinelearning, multimedia, and computer vision. Although many algorithms have beencontinuously proposed in the literature in the above domains each year, thereis no comprehensive evaluation and analysis of their performances.  In this paper, we conduct a comprehensive experimental evaluation of manystate-of-the-art methods for approximate nearest neighbor search. Our study (1)is cross-disciplinary (i.e., including 16 algorithms in different domains, andfrom practitioners) and (2) has evaluated a diverse range of settings,including 20 datasets, several evaluation metrics, and different queryworkloads. The experimental results are carefully reported and analyzed tounderstand the performance results. Furthermore, we propose a new method thatachieves both high query efficiency and high recall empirically on majority ofthe datasets under a wide range of settings.

Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)