Space partitions of Rd 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 et al., 2018b;c), 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, 2013) and neural networks, respectively, to obtain a new partitioning procedure called Neural Locality-Sensitive Hashing (Neural LSH). On several standard benchmarks for NNS (Aumüller et al., 2017), 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.
|State||Published - 2020|
|Event||8th International Conference on Learning Representations, ICLR 2020 - Addis Ababa, Ethiopia|
Duration: 30 Apr 2020 → …
|Conference||8th International Conference on Learning Representations, ICLR 2020|
|Period||30/04/20 → …|