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• W1732650778 abstract "The $k$-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct $k$-NN graphs remains a challenge, especially for large-scale high-dimensional data. In this paper, we propose a new approach to construct approximate $k$-NN graphs with emphasis in: efficiency and accuracy. We hierarchically and randomly divide the data points into subsets and build an exact neighborhood graph over each subset, achieving a base approximate neighborhood graph; we then repeat this process for several times to generate multiple neighborhood graphs, which are combined to yield a more accurate approximate neighborhood graph. Furthermore, we propose a neighborhood propagation scheme to further enhance the accuracy. We show both theoretical and empirical accuracy and efficiency of our approach to $k$-NN graph construction and demonstrate significant speed-up in dealing with large scale visual data." @default.
• W1732650778 created "2016-06-24" @default.
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• W1732650778 date "2013-07-30" @default.
• W1732650778 modified "2023-09-25" @default.
• W1732650778 title "Scalable $k$-NN graph construction" @default.
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• W1732650778 doi "https://doi.org/10.48550/arxiv.1307.7852" @default.
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