Knowledge Commons of Institute of Automation,CAS
A Peer-to-Peer Distributed Bisecting K-means | |
Gao HY(高浩元)1,2![]() | |
2022-04 | |
会议名称 | International Conference on Machine Learning and Computing |
会议日期 | 2022-2-19 |
会议地点 | 线上 |
摘要 | Distributed machine learning over peer-to-peer network has become popular in the past few years due to the growing demand for privacy protection. Recent peer-to-peer distributed K-means algorithm can achieve the same performance as centralized K-means, but they also has high sensitivity to initialization as centralized K-means, which worsens its performance for clustering. In this paper, we first proposes a distributed bisecting K-means algorithm over a peer-to-peer network to alleviate this drawback by combining bisecting K-means with Metropolis algorithm, since the previous works showed that bisecting K-means is much less sensitive to initialization than traditional K-means. It is shown by extensive simulations that our algorithm has the same performance with centralized bisecting K-means and outperforms the existing peer-to-peer distributed K-means. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48805 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Gao HY. A Peer-to-Peer Distributed Bisecting K-means[C],2022. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
A_Peer_to_Peer_Distr(4307KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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