Adjusting Learning Depth in Nonnegative Latent Factorization of Tensors for Accurately Modeling Temporal Patterns in Dynamic QoS Data

Xin Luo, Minzhi Chen, Hao Wu, Zhigang Liu, Huaqiang Yuan, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

Abstract

A nonnegative latent factorization of tensors (NLFT) model precisely represents the temporal patterns hidden in multichannel data emerging from various applications. It often adopts a single latent factor-dependent, nonnegative and multiplicative update on tensor (SLF-NMUT) algorithm. However, learning depth in this algorithm is not adjustable, resulting in frequent training fluctuation or poor model convergence caused by overshooting. To address this issue, this study carefully investigates the connections between the performance of an NLFT model and its learning depth via SLF-NMUT to present a joint learning-depth-adjusting scheme for it. Based on this scheme, a Depth-adjusted Multiplicative Update on tensor algorithm is innovatively proposed, thereby achieving a novel depth-adjusted nonnegative latent-factorization-of-tensors (DNL) model. Empirical studies on two industrial data sets demonstrate that compared with the state-of-the-art NLFT models, a DNL model achieves significant accuracy gain when performing missing data estimation on a high-dimensional and incomplete tensor with high efficiency.

Original languageEnglish (US)
JournalIEEE Transactions on Automation Science and Engineering
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Algorithm
  • Analytical models
  • Computational modeling
  • Data models
  • Quality of service
  • Tensors
  • Training
  • Web services
  • big data
  • dynamics
  • high-dimensional and incomplete (HDI) data
  • machine learning
  • missing data estimation
  • multichannel data
  • nonnegative latent factorization of tensors (NLFT)
  • quality of service (QoS)
  • temporal pattern
  • web service.

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