图 书 馆
可扩展的k‑意味着通过轻量级核心集进行集群Scalable k‑Means Clustering via Lightweight Coresets |
|
| 课程网址: | http://videolectures.net/kdd2018_bachem_lightweight_coresets/ |
| 主讲教师: | Olivier Bachem |
| 开课单位: | 苏黎世联邦理工学院 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Coresets are compact representations of data sets such that models trained on a coreset are provably competitive with models trained on the full data set. As such, they have been successfully used to scale up clustering models to massive data sets. While existing approaches generally only allow for multiplicative approximation errors, we propose a novel notion of lightweight coresets that allows for both multiplicative and additive errors. We provide a single algorithm to construct lightweight coresets for k -means clustering as well as soft and hard Bregman clustering. The algorithm is substantially faster than existing constructions, embarrassingly parallel, and the resulting coresets are smaller. We further show that the proposed approach naturally generalizes to statistical k -means clustering and that, compared to existing results, it can be used to compute smaller summaries for empirical risk minimization. In extensive experiments, we demonstrate that the proposed algorithm outperforms existing data summarization strategies in practice. |
| 关 键 词: | 聚类模型; 风险最小化; 数据摘要 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-19：chenjy |
| 最后编审: | 2023-05-11：chenjy |
| 阅读次数: | 36 |