Pruned Bi-directed K-nearest Neighbor Graph for Proximity Search

  • Masajiro IwasakiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9939)


In this paper, we address the problems with fast proximity searches for high-dimensional data by using a graph as an index. Graph-based methods that use the k-nearest neighbor graph (KNNG) as an index perform better than tree-based and hash-based methods in terms of search precision and query time. To further improve the performance of the KNNG, the number of edges should be increased. However, increasing the number takes up more memory, while the rate of performance improvement gradually falls off. Here, we propose a pruned bi-directed KNNG (PBKNNG) in order to improve performance without increasing the number of edges. Different directed edges for existing edges between a pair of nodes are added to the KNNG, and excess edges are selectively pruned from each node. We show that the PBKNNG outperforms the KNNG for SIFT and GIST image descriptors. However, the drawback of the KNNG is that its construction cost is fatally expensive. As an alternative, we show that a graph can be derived from an approximate neighborhood graph, which costs much less to construct than a KNNG, in the same way as the PBKNNG and that it also outperforms a KNNG.


K-nearest Neighbor Graph (KNNG) Proximity Search Excessive Edge Query Time High Accuracy Range 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Yahoo Japan CorporationTokyoJapan

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