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arXiv · 1908.10396

Accelerating Large-Scale Inference with Anisotropic Vector Quantization

Ruiqi Guo·Philip Sun·Erik Lindgren·Quan Geng·David Simcha·Felix Chern·Sanjiv Kumar
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Abstract

Quantization based techniques are the current state-of-the-art for scaling maximum inner product search to massive databases. Traditional approaches to quantization aim to minimize the reconstruction error of the database points. Based on the observation that for a given query, the database points that have the largest inner products are more relevant, we develop a family of anisotropic quantization loss functions. Under natural statistical assumptions, we show that quantization with these loss functions leads to a new variant of vector quantization that more greatly penalizes the parallel component of a datapoint's residual relative to its orthogonal component. The proposed approach achieves state-of-the-art results on the public benchmarks available at \url{ann-benchmarks.com}.

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Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar. 2019-08-27. Accelerating Large-Scale Inference with Anisotropic Vector Quantization. https://arxiv.org/abs/1908.10396

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