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 . 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 this general approach with the KaHIP graph partitioner [Sanders, Schulz SEA 2013] and neural 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 found by Neural LSH consistently outperform partitions found by quantization- and tree-based methods.
Space partitions of \mathbb{R 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 bysupervised classification. We instantiate this general approach with the KaHIP graph partitioner [Sanders, Schulz SEA 2013] and neural 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.
R
Most of the efficient sublinear-time indexing algorithms for the high-dimensional
Space partitions of \mathbb{R
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problem
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are based on space partitions of the ambient space \mathbb{R
algorithms
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constructing such partitions that reduces
building space partitions reducing
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balanced graph partitioning followed by supervised classification.
balanced graph partitioning
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followed by
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supervised classification.
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found
obtained
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quantization-
quantization-based