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A Generalized Nesterov's Accelerated Gradient-Incorporated Non-Negative Latent-Factorization-of-Tensors Model for Efficient Representation to Dynamic QoS Data
Chen, Minzhi1,2; Wang, Renfang3; Qiao, Yan4,5; Luo, Xin6
2024-03-04
摘要Dynamic Quality-of-Service (QoS) data can be efficiently represented by a Non-negative Latent-factorization-of-tensors model, which relies on a Non-negative and Multiplicative Update on Incomplete Tensors (NMU-IT) algorithm. Nevertheless, NMU-IT frequently encounters slow convergence and inefficient hyper-parameters selection. Targeting at overcome these critical defects, this paper proposed to improve the NMU-IT algorithm from two perspectives: a) integrating a generalized Nesterov's accelerated gradient method to accelerate the resultant model's convergence rate, and b) establishing the hyper-parameter adaptation mechanism through the particle swarm optimization strategy. On the basis of these conceptions, this study successfully builds a Generalized Nesterov's Accelerated Gradient-incorporated Non-negative Latent-factorization-of-tensors (GNL) model for precisely and high-efficiently representing the dynamic QoS data. The proposed GNL model has shown its superiority over several advanced models concerning both the precision of estimating missing QoS data and training efficiency, as demonstrated by the experiments conducted on two dynamic QoS datasets.
关键词Cloud service dynamic QoS data high-dimensional and incomplete tensor non-negative latent-factorization-of-tensors particle swarm optimization nesterov's accelerated gradient method linear bias adaptive model
DOI10.1109/TETCI.2024.3360338
发表期刊IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
ISSN2471-285X
页码15
通讯作者Wang, Renfang(wangrf@zwu.edu.cn) ; Luo, Xin(luoxin21@gmail.com)
收录类别SCI
WOS记录号WOS:001181555000001
语种英语