Structural tensor-on-tensor regression with interaction effects and its application to a hot rolling process

Huihui Miao, Andi Wang, Bing Li, Jianjun Shi

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a method of Structural Tensor-On-Tensosr regression considering the Interaction effects (STOTI). To alleviate the curse of dimensionality and resolve computational challenge, the STOTI method describes the specific structure of the main and interaction effect tensors indicated by the prior knowledge of the data using corresponding regularization terms on their appropriate modes. We designed an ADMM consensus algorithm to estimate these coefficient tensors. Extensive simulations and a real case study of the hot rolling process verified the superiority of the proposed method in terms of estimation and prediction accuracy.

Original languageEnglish (US)
JournalJournal of Quality Technology
DOIs
StateAccepted/In press - 2021
Externally publishedYes

Keywords

  • interaction effect
  • regularization
  • tensor-on-tensor regression

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Fingerprint

Dive into the research topics of 'Structural tensor-on-tensor regression with interaction effects and its application to a hot rolling process'. Together they form a unique fingerprint.

Cite this