The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Optimized Product Quantization for Approximate Nearest Neighbor Search
Abstract
Product quantization is an effective vector quantization approach to compactly encode high-dimensional vectors for fast approximate nearest neighbor (ANN) search. The essence of product quantization is to decompose the original high-dimensional space into the Cartesian product of a finite number of low-dimensional subspaces that are then quantized separately. Optimal space decomposition is important for the performance of ANN search, but still remains unaddressed. In this paper, we optimize product quantization by minimizing quantization distortions w.r.t. the space decomposition and the quantization codebooks. We present two novel methods for optimization: a non-parametric method that alternatively solves two smaller sub-problems, and a parametric method that is guaranteed to achieve the optimal solution if the input data follows some Gaussian distribution. We show by experiments that our optimized approach substantially improves the accuracy of product quantization for ANN search.
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Product Quantization for Nearest Neighbor Search
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“Experiments show that our two solutions outperform the original PQ [10] and other alternatives like transform coding [3] and iterative quantization [6], even when the prior knowledge about the structure of the input data is used by PQ [10].”
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Zhang, D., & Li, W.-J. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8995 · Cited by 616
Douze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazaré, P.-E., Lomeli, M., Hosseini, L., & Jégou, H. (2026) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3618474 · Cited by 478
K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes
He, K., Wen, F., & Sun, J. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.378 · Cited by 395
Self-citationSurvey of Vector Database Management Systems
Pan, J. J., Wang, J., & Li, G. (2023) · arXiv · doi:10.48550/arxiv.2310.14021 · Cited by 184
Human-inspired Perspectives: A Survey on AI Long-term Memory
He, Z., Lin, W., Zheng, H., Zhang, F., Jones, M. W., Aitchison, L., Xu, X., Liu, M., Kristensson, P. O., & Shen, J. (2024) · arXiv · doi:10.48550/arxiv.2411.00489 · Cited by 3
“The following subsections provide a detailed discussion of the storage, retrieval, and forgetting processes of parametric memory.”
Metadata
- Authors
- Ge, T., He, K., Ke, Q., & Sun, J.
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 2946-2953
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2111006384
- Cited by
- 384
- References
- 18
- FWCI
- 20.27
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ge, T., He, K., Ke, Q., & Sun, J. (2013). Optimized Product Quantization for Approximate Nearest Neighbor Search. 2013 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2013.379
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