Corpus ID: 59222824

Learning Sublinear-Time Indexing for Nearest Neighbor Search

@article{Dong2019LearningSI,
  title={Learning Sublinear-Time Indexing for Nearest Neighbor Search},
  author={Yihe Dong and P. Indyk and Ilya P. Razenshteyn and Tal Wagner},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.08544}
}
Most of the efficient sublinear-time indexing algorithms for the high-dimensional nearest neighbor search problem (NNS) are based on space partitions of the ambient space $\mathbb{R}^d$. Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for constructing such partitions that reduces the problem to balanced graph partitioning followed by supervised classification. We instantiate… Expand

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