Learning Space Partitions for Nearest Neighbor Search [article]

Yihe Dong and Piotr Indyk and Ilya Razenshteyn and Tal Wagner
2020 arXiv   pre-print
Space partitions of ℝ^d underlie a vast and important class of fast nearest neighbor search (NNS) algorithms. 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 building space partitions reducing the problem to balanced graph partitioning followed by supervised classification. We instantiate this general approach with the KaHIP graph partitioner [Sanders, Schulz SEA 2013] and
more » ... eural networks, respectively, to obtain a new partitioning procedure called Neural Locality-Sensitive Hashing (Neural LSH). On several standard benchmarks for NNS, our experiments show that the partitions obtained by Neural LSH consistently outperform partitions found by quantization-based and tree-based methods as well as classic, data-oblivious LSH.
arXiv:1901.08544v4 fatcat:gyt2wqp6uvamnjckbnggqilszi