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Product quantization (PQ) is a technique for approximate nearest neighbor search.[1] It works by lossily compressing vectors by representing them as a Cartesian product of low-dimensional subspaces and quantizing each subspace independently.[2] Distances can be efficiently computed between product-quantized vectors and an unquantized vector by creating a lookup table, so product quantization can save compute, storage and memory bandwidth.[3]
Product quantization can be used by itself or as a component of more complex ANN search algorithms.[4]
Optimized product quantization (OPQ)
- ^ Jégou, Herve; Douze, Matthijs; Schmid, Cordelia (2010-03-18). "Product Quantization for Nearest Neighbor Search". IEEE Transactions on Pattern Analysis and Machine Intelligence. 33 (1): 117–128. doi:10.1109/TPAMI.2010.57. Retrieved 13 April 2025.
- ^ Wu, Ze-bin; Yu, Jun-qing (2019-05-18). "Vector quantization: a review". Frontiers of Information Technology & Electronic Engineering. 20: 507–524. doi:10.1631/FITEE.1700833.
- ^ . arXiv:1908.10396.
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