An Accurate and Efficient Large-Scale Regression Method Through Best Friend Clustering

doi:10.1109/TPDS.2021.3134336

	An Accurate and Efficient Large-Scale Regression Method Through Best Friend Clustering
	Li, Kun 1,2; Yuan, Liang 1; Zhang, Yunquan 1; Chen, Gongwei 2,3
	2022-11-01
发表期刊	IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
ISSN	1045-9219
卷号	33 期号:11 页码:3129-3140
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.
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
DOI	10.1109/TPDS.2021.3134336
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000800198000038
出版者	IEEE COMPUTER SOC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19583
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.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci UCAS, Sch Comp Sci & Technol, 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.