Fast nearest neighbor search based on approximate k-NN graph

Abstract

Nearest neighbor search is known as a challenging issue that has been studied for several decades. Recently, this issue becomes more and more imminent in viewing that the big data problem arises from various fields. According to recent study, graph-based methods are effective to address this issue. However, the k-nearest neighbor graph construction of the existing solutions is computationally inefficient, which becomes the processing bottleneck. To address this issue, a novel k-nearest neighbor graph construction method is proposed. As indicated by extensive experiments, satisfactory performance is achieved on different datasets while the graph construction cost has been reduced to low level. In addition, a comparative study on various approximate nearest neighbor search methods, such as the space partitioning, compressional, hash and the graph-based is presented. As indicated in the experiment, our method makes the best trade-off between search quality and computational cost.