Turning Big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering

Author:

Feldman Dan,Schmidt Melanie,Sohler Christian

Publisher

Society for Industrial and Applied Mathematics

Cited by 75 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. On resolution coresets for constrained clustering;Journal of Computational Mathematics and Data Science;2024-09

2. On Optimal Coreset Construction for Euclidean (k,z)-Clustering;Proceedings of the 56th Annual ACM Symposium on Theory of Computing;2024-06-10

3. Toward General Function Approximation in Nonstationary Reinforcement Learning;IEEE Journal on Selected Areas in Information Theory;2024

4. Optimal sampling designs for multidimensional streaming time series with application to power grid sensor data;The Annals of Applied Statistics;2023-12-01

5. Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation;2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS);2023-11-06

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