An Accurate and Efficient Large-Scale Regression Method Through Best Friend Clustering
文献类型：期刊论文
| 作者 | Li, Kun1,2; Yuan, Liang2; Zhang, Yunquan2; Chen, Gongwei1,3 |
| 刊名 | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
 |
| 出版日期 | 2022-11-01 |
| 卷号 | 33期号:11页码:3129-3140 |
| 关键词 | Clustering algorithms Training Mathematical models Computational modeling Libraries Kernel Support vector machines Distributed machine learning scalable algorithm large-scale clustering parallel regression |
| ISSN号 | 1045-9219 |
| DOI | 10.1109/TPDS.2021.3134336 |
| 英文摘要 | As the data size in Machine Learning fields grows exponentially, it is inevitable to accelerate the computation by utilizing the ever-growing large number of available cores provided by high-performance computing hardware. However, existing parallel methods for clustering or regression often suffer from problems of low accuracy, slow convergence, and complex hyperparameter-tuning. Furthermore, the parallel efficiency is usually difficult to improve while striking a balance between preserving model properties and partitioning computing workloads on distributed systems. In this article, we propose a novel and simple data structure capturing the most important information among data samples. It has several advantageous properties supporting a hierarchical clustering strategy that contains well-defined metrics for determining optimal hierarchy, balanced partition for maintaining the clustering property, and efficient parallelization for accelerating computation phases. Then we combine the clustering with regression techniques as a parallel library and utilize a hybrid structure of data and model parallelism to make predictions. Experiments illustrate that our library obtains remarkable performance on convergence, accuracy, and scalability. |
| 资助项目 | National Natural Science Foundation of China[61972376] ; National Natural Science Foundation of China[62072431] ; National Natural Science Foundation of China[62032023] ; Science Foundation of Beijing[L182053] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000800198000038 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19582]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Yuan, Liang |
| 作者单位 | 1.Univ Chinese Acad Sci UCAS, Sch Comp Sci & Technol, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Kun,Yuan, Liang,Zhang, Yunquan,et al. An Accurate and Efficient Large-Scale Regression Method Through Best Friend Clustering[J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,2022,33(11):3129-3140. |
| APA | Li, Kun,Yuan, Liang,Zhang, Yunquan,&Chen, Gongwei.(2022).An Accurate and Efficient Large-Scale Regression Method Through Best Friend Clustering.IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,33(11),3129-3140. |
| MLA | Li, Kun,et al."An Accurate and Efficient Large-Scale Regression Method Through Best Friend Clustering".IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 33.11(2022):3129-3140. |
入库方式： OAI收割
来源：计算技术研究所
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