Advancing Non-negative Latent Factorization of Tensors with Diversified Regularizations

Hao Wu, Xin Luo, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

16 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 study investigates NLFT models with diversified regularization schemes. Six regularized NLFT models, i.e., 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)
JournalIEEE Transactions on Services Computing
StateAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

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


  • Big Data
  • Computational modeling
  • Data models
  • Ensemble
  • High-dimensional and Sparse Tensor
  • Latent Factor Analysis
  • Missing Data
  • Non-negative Latent Factorization of Tensor
  • Non-negativity
  • Predictive models
  • Quality of service
  • Regularization
  • Services Computing
  • Temporal Pattern
  • Tensile stress
  • Web services


Dive into the research topics of 'Advancing Non-negative Latent Factorization of Tensors with Diversified Regularizations'. Together they form a unique fingerprint.

Cite this