Momentum-incorporated latent factorization of tensors for extracting temporal patterns from QoS data

doi:10.1109/SMC.2019.8914594

	Momentum-incorporated latent factorization of tensors for extracting temporal patterns from QoS data
	Chen, Minzhi 2; Wu, Hao 1,3; He, Chunlin 2; Chen, Sili 2
	2019
会议日期	October 6, 2019 - October 9, 2019
会议地点	Bari, Italy
DOI	10.1109/SMC.2019.8914594
页码	1757-1762
英文摘要	Quality-of-service (QoS) of Web services vary over time, making it a significant issue to discover temporal patterns from them for addressing various subsequent analyzing tasks like missing QoS prediction. A Latent factorization of tensors (LFT)-based approach proves to be highly efficient in addressing this issue, which can be built through a stochastic gradient descent (SGD) solver efficiently. However, an SGD-based LFT model frequently suffers low-tail convergence. For addressing this issue, we present a momentum-incorporated latent factorization of tensors (MLFT) model, which integrates a momentum method into an SGD-based LFT model, thereby improving its convergence rate as well as maintaining the prediction accuracy for missing QoS data. Empirical studies on two dynamic industrial QoS datasets show that compared with an SGD-based LFT model, an MLFT model achieves faster convergence rate and higher prediction accuracy. © 2019 IEEE.
会议录	2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
语种	英语
ISSN号	1062922X
内容类型	会议论文
源URL	[http://119.78.100.138/handle/2HOD01W0/9784]
专题	中国科学院重庆绿色智能技术研究院
作者单位	1.University of Chinese Academy of Sciences, Beijing; 100049, China 2.Computer School of China West Normal University, Nanchong, Sichuan; 637002, China; 3.Chongqing Key Laboratory of Big Data and Intelligent Computing, Chinese Academy of Sciences, Chongqing Institute of Green and Intelligent Technology, Chongqing; 400714, China;
推荐引用方式 GB/T 7714	Chen, Minzhi,Wu, Hao,He, Chunlin,et al. Momentum-incorporated latent factorization of tensors for extracting temporal patterns from QoS data[C]. 见:. Bari, Italy. October 6, 2019 - October 9, 2019.