Advancing Non-Negative Latent Factorization of Tensors With Diversified Regularization Schemes

Hao Wu, Xin Luo, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

80 Scopus citations


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., L2, L1 L2,L1, 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.

Original languageEnglish (US)
Pages (from-to)1334-1344
Number of pages11
JournalIEEE Transactions on Services Computing
Issue number3
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems and Management


  • High-dimensional and sparse tensor
  • ensemble
  • latent factor analysis
  • missing data
  • non-negative latent factorization of tensor
  • non-negativity
  • regularization
  • services computing
  • temporal pattern


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