On the Merge of k-NN Graph

k-nearest neighbor graph is the fundamental data structure in many disciplines such as information retrieval, data-mining, pattern recognition and machine learning, etc. In the literature, considerable research has been focusing on how to efficiently build an approximate k-nearest neighbor graph (k-NN graph) for a fixed dataset. Unfortunately, a closely related issue to the graph construction has been long overlooked. Namely, few literature covers about how to merge two existing k-NN graphs. In this paper, we address the k-NN graph merge issue of two different scenarios. On the first hand, peer merge is proposed to address the problem of merging two approximate k-NN graphs into one. This makes parallel approximate k-NN graph computation in large-scale become possible. In addition, the problem of merging a raw set into a built k-NN graph is also addressed by joint merge. It allows the approximate k-NN graph to be built incrementally. It therefore supports approximate k-NN graph construction for an open set. Moreover, deriving from joint merge, an hierarchical approximate k-NN graph construction approach is presented. With the support of produced graph hierarchy, superior performance is observed on the large-scale NN search task across various data types and data dimensions, and under different distance measures.