Balancing Global and Local: Representative Sampling for Large-Scale Vector Data
Summary: Jointly optimizes nearest-sample global coverage and matched-scale local intrinsic dimensionality (LID) fidelity, proving representative sampling NP-hard. LASS-Lite/LASS-NA combine LID-stratified coverage with shared-neighbor submodular selection, achieving a (1−1/e) guarantee and strong empirical gains. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Zheng Wu (Hong Kong Baptist University)
- 2. Yitong Song (Hong Kong Baptist University)
- 3. Xuliang Zhu (Shanghai Jiao Tong University)
- 4. Huiling Li (Hong Kong Baptist University)
- 5. Jianliang Xu (Hong Kong Baptist University)
- 6. Xin Huang (Hong Kong Baptist University)
BibTeX Citation
@inproceedings{wu_sigmod26,
title = {{Balancing Global and Local: Representative Sampling for Large-Scale Vector Data}},
author = {Wu, Zheng and Song, Yitong and Zhu, Xuliang and Li, Huiling and Xu, Jianliang and Huang, Xin},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802019},
url = {https://dl.acm.org/doi/10.1145/3802019},
year = {2026}
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