A general framework for predictive tensor modeling with domain knowledge

Yada Zhu, Jingrui He, Richard D. Lawrence

Research output: Contribution to journalArticlepeer-review


In many real applications such as virtual metrology in semiconductor manufacturing, face recognition, and gait recognition in computer vision, the input data is naturally expressed as tensors or multi-dimensional arrays. Furthermore, in addition to the known label information, domain knowledge can often be obtained from various sources, e.g., multiple domain experts. To address such problems, in this paper, we propose a general optimization framework for dealing with tensor inputs while taking into consideration domain knowledge. To be specific, our framework is based on a linear model, and we obtain the weight tensor in a hierarchical way—first approximate it by a low-rank tensor, and then estimate the low-rank approximation using the domain knowledge from various sources. This is motivated by wafer quality prediction in semiconductor manufacturing. We also propose an effective algorithm named H-MOTE for solving this framework, which is guaranteed to converge. For each iteration, the time complexity of H-MOTE is linear with respect to the number of examples as well as the size of the weight tensor. Therefore, H-MOTE is scalable to large-scale problems. Experimental results show that H-MOTE outperforms state-of-the-art techniques on both synthetic and real data sets.

Original languageEnglish (US)
Pages (from-to)1709-1732
Number of pages24
JournalData Mining and Knowledge Discovery
Issue number6
StatePublished - Nov 29 2015
Externally publishedYes


  • Algorithms
  • Classifier design and evaluation
  • Data mining
  • Semiconductor manufacturing
  • Tensor
  • Virtual metrology
  • Wafer quality

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications


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