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arXiv · 1908.00814

On the Merge of k-NN Graph

Wan-Lei Zhao·Hui Wang·Peng-Cheng Lin·Chong-Wah Ngo
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Abstract

k-nearest neighbor graph is a 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 of how to merge two existing k-NN graphs has been overlooked. In this paper, we address the issue of k-NN graph merging in two different scenarios. In the first scenario, a symmetric merge algorithm is proposed to combine two approximate k-NN graphs. The algorithm facilitates large-scale processing by the efficient merging of k-NN graphs that are produced in parallel. In the second scenario, a joint merge algorithm is proposed to expand an existing k-NN graph with a raw dataset. The algorithm enables the incremental construction of a hierarchical approximate k-NN graph. Superior performance is attained when leveraging the hierarchy for NN search of various data types, dimensionality, and distance measures.

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Wan-Lei Zhao, Hui Wang, Peng-Cheng Lin, Chong-Wah Ngo. 2019-08-02. On the Merge of k-NN Graph. https://arxiv.org/abs/1908.00814

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