Effective and General Distance Computation for Approximate Nearest Neighbor Search
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929680406282240 |
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| author | Yang, Mingyu Li, Wentao Jin, Jiabao Zhong, Xiaoyao Wang, Xiangyu Shen, Zhitao Jia, Wei Wang, Wei |
| author_facet | Yang, Mingyu Li, Wentao Jin, Jiabao Zhong, Xiaoyao Wang, Xiangyu Shen, Zhitao Jia, Wei Wang, Wei |
| contents | Approximate K Nearest Neighbor (AKNN) search in high-dimensional spaces is a critical yet challenging problem. In AKNN search, distance computation is the core task that dominates the runtime. Existing approaches typically use approximate distances to improve computational efficiency, often at the cost of reduced search accuracy. To address this issue, the state-of-the-art method, ADSampling, employs random projections to estimate approximate distances and introduces an additional distance correction process to mitigate accuracy loss. However, ADSampling has limitations in both effectiveness and generality, primarily due to its reliance on random projections for distance approximation and correction. To address the effectiveness limitations of ADSampling, we leverage data distribution to improve distance computation via orthogonal projection. Furthermore, to overcome the generality limitations of ADSampling, we adopt a data-driven approach to distance correction, decoupling the correction process from the distance approximation process. Extensive experiments demonstrate the superiority and effectiveness of our method. In particular, compared to ADSampling, our method achieves a speedup of 1.6 to 2.1 times on real-world datasets while providing higher accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16322 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Effective and General Distance Computation for Approximate Nearest Neighbor Search Yang, Mingyu Li, Wentao Jin, Jiabao Zhong, Xiaoyao Wang, Xiangyu Shen, Zhitao Jia, Wei Wang, Wei Databases Approximate K Nearest Neighbor (AKNN) search in high-dimensional spaces is a critical yet challenging problem. In AKNN search, distance computation is the core task that dominates the runtime. Existing approaches typically use approximate distances to improve computational efficiency, often at the cost of reduced search accuracy. To address this issue, the state-of-the-art method, ADSampling, employs random projections to estimate approximate distances and introduces an additional distance correction process to mitigate accuracy loss. However, ADSampling has limitations in both effectiveness and generality, primarily due to its reliance on random projections for distance approximation and correction. To address the effectiveness limitations of ADSampling, we leverage data distribution to improve distance computation via orthogonal projection. Furthermore, to overcome the generality limitations of ADSampling, we adopt a data-driven approach to distance correction, decoupling the correction process from the distance approximation process. Extensive experiments demonstrate the superiority and effectiveness of our method. In particular, compared to ADSampling, our method achieves a speedup of 1.6 to 2.1 times on real-world datasets while providing higher accuracy. |
| title | Effective and General Distance Computation for Approximate Nearest Neighbor Search |
| topic | Databases |
| url | https://arxiv.org/abs/2404.16322 |