Scalable $k$-NN graph construction
Abstract
The -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 -NN graphs remains a challenge, especially for large-scale high-dimensional data. In this paper, we propose a new approach to construct approximate -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 -NN graph construction and demonstrate significant speed-up in dealing with large scale visual data.
Cite
@article{arxiv.1307.7852,
title = {Scalable $k$-NN graph construction},
author = {Jingdong Wang and Jing Wang and Gang Zeng and Zhuowen Tu and Rui Gan and Shipeng Li},
journal= {arXiv preprint arXiv:1307.7852},
year = {2013}
}