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	Advancing Non-Negative Latent Factorization of Tensors With Diversified Regularization Schemes
	Wu, Hao1,2,3; Luo, Xin1,2,4; Zhou, Mengchu5,6,7
	2022-05-01
摘要	Dynamic relationships are frequently encountered in big data and services computing-related applications, like dynamic data of user-side QoS in Web services. They are modeled into a high-dimensional and sparse (HiDS) tensor, which contain rich knowledge regarding temporal patterns. A non-negative latent factorization of tensors (NLFT) model is very effective in extracting such patterns from an HiDS tensor. However, it commonly suffers from overfitting with improper regularization schemes. To address this issue, this article investigates NLFT models with diversified regularization schemes. Six regularized NLFT models, i.e., L-2, L-1, elastic net, log, dropout, and swish-regularized ones, are proposed and carefully investigated. Moreover, owing to their diversified regularization designs, they possess strong model diversity to achieve an effective ensemble. Empirical studies on HiDS QoS tensors from real applications demonstrate that compared with state-of-the-art models, the proposed ones better describe the temporal patterns hidden in an HiDS tensor, thereby achieving significantly higher prediction accuracy for missing data. Moreover, their ensemble further outperforms each of them in terms of prediction accuracy for missing QoS data.
关键词	High-dimensional and sparse tensor missing data latent factor analysis temporal pattern non-negativity non-negative latent factorization of tensor regularization ensemble services computing
DOI	10.1109/TSC.2020.2988760
发表期刊	IEEE TRANSACTIONS ON SERVICES COMPUTING
ISSN	1939-1374
卷号	15期号:3页码:1334-1344
通讯作者	Luo, Xin(luoxin21@cigit.ac.cn)
收录类别	SCI
WOS记录号	WOS:000812532400013
语种	英语