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Data"],"published-print":{"date-parts":[[2026,5,18]]},"abstract":"<jats:p>\n                    Representative sampling, which extracts a small subset of representative instances from massive, high-dimensional vector data, is useful for many applications, such as visualization, model prototyping, and data exploration. Existing methods often suffer from either\n                    <jats:italic toggle=\"yes\">over-representing dense regions while neglecting sparse ones<\/jats:italic>\n                    or\n                    <jats:italic toggle=\"yes\">failing to capture fine-grained data distributions<\/jats:italic>\n                    . To address these challenges, we study representative sampling with two objectives:\n                    <jats:italic toggle=\"yes\">global coverage<\/jats:italic>\n                    and\n                    <jats:italic toggle=\"yes\">local fidelity<\/jats:italic>\n                    . Specifically, given a dataset\n                    <jats:italic toggle=\"yes\">D<\/jats:italic>\n                    and a sampling ratio \u03c1 \u2208 (0, 1], we seek a subset\n                    <jats:italic toggle=\"yes\">S<\/jats:italic>\n                    \u2286\n                    <jats:italic toggle=\"yes\">D<\/jats:italic>\n                    with |\n                    <jats:italic toggle=\"yes\">S<\/jats:italic>\n                    |=\u03c1|\n                    <jats:italic toggle=\"yes\">D<\/jats:italic>\n                    | that minimizes the local-fidelity cost subject to a global-coverage constraint. Here, the global coverage of\n                    <jats:italic toggle=\"yes\">S<\/jats:italic>\n                    measures the average distance from data points to their nearest samples in\n                    <jats:italic toggle=\"yes\">S<\/jats:italic>\n                    . We define local fidelity by comparing local intrinsic dimensionality (LID) estimates computed on\n                    <jats:italic toggle=\"yes\">S<\/jats:italic>\n                    and on the full dataset\n                    <jats:italic toggle=\"yes\">D<\/jats:italic>\n                    at a matched evaluation scale, which measures how well fine-grained local distributions are preserved. Unfortunately, efficiently computing an optimal solution is challenging; we prove that the representative sampling problem is NP-hard.\n                  <\/jats:p>\n                  <jats:p>\n                    To address this challenge, we first propose LASS-Lite, a heuristic that improves global coverage by selecting widely separated initial samples across diverse LID levels and then enhances local fidelity by adding their nearby neighbors. Since LASS-Lite selects neighbors for each sample independently, we further propose LASS-NA, which employs a more effective joint selection strategy by prioritizing points that can serve as shared neighbors for multiple samples. This approach better utilizes the sampling budget to improve overall local fidelity. We formulate this strategy as a submodular maximization problem, yielding an efficient greedy algorithm with a provable (1-1\/\n                    <jats:italic toggle=\"yes\">e<\/jats:italic>\n                    )-approximation guarantee. To validate the effectiveness of our methods, we conduct extensive experiments on four large-scale, real-world vector datasets. In terms of local fidelity, our algorithms use only 5.0% of the data and reduce the local fidelity cost by 58.6%-62.7% compared to three state-of-the-art competitors at similar levels of global coverage. In addition, we demonstrate the practical utility of our representative sampling in data visualization, self-supervised model prototyping, graph-based approximate nearest neighbor search (ANNS) and label-efficient model selection. Our source code is available at https:\/\/github.com\/i11ume\/LASS.\n                  <\/jats:p>","DOI":"10.1145\/3802019","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T18:19:16Z","timestamp":1779128356000},"page":"1-27","source":"Crossref","is-referenced-by-count":0,"title":["Balancing Global and Local: Representative Sampling for Large-Scale Vector Data"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9207-1962","authenticated-orcid":false,"given":"Zheng","family":"Wu","sequence":"first","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4220-0145","authenticated-orcid":false,"given":"Yitong","family":"Song","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0655-8809","authenticated-orcid":false,"given":"Xuliang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2815-7598","authenticated-orcid":false,"given":"Huiling","family":"Li","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9404-5848","authenticated-orcid":false,"given":"Jianliang","family":"Xu","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3650-0301","authenticated-orcid":false,"given":"Xin","family":"Huang","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Sameer Agarwal and et al. 2013. 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