Hierarchical modeling with tensor inputs

Yada Zhu, Jingrui He, Rick Lawrence

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In many real applications, the input data are naturally expressed as tensors, such as virtual metrology in semiconductor manufacturing, face recognition and gait recognition in computer vision, etc. In this paper, we propose a general optimization framework for dealing with tensor inputs. Most existing methods for supervised tensor learning use only rank-one weight tensors in the linear model and cannot readily incorporate domain knowledge. In our framework, we obtain the weight tensor in a hierarchical way - we first approximate it by a low-rank tensor, and then estimate the low-rank approximation using the prior knowledge from various sources, e.g., different domain experts. This is motivated by wafer quality prediction in semiconductor manufacturing. Furthermore, we propose an effective algorithm named H-MOTE for solving this framework, which is guaranteed to converge. The time complexity of H-MOTE is linear with respect to the number of examples as well as the size of the weight tensor. Experimental results show the superiority of H-MOTE over state-of-the-art techniques on both synthetic and real data sets.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Pages1233-1239
Number of pages7
Volume2
StatePublished - 2012
Externally publishedYes
Event26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12 - Toronto, ON, Canada
Duration: Jul 22 2012Jul 26 2012

Other

Other26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12
CountryCanada
CityToronto, ON
Period7/22/127/26/12

Fingerprint

Tensors
Semiconductor materials
Face recognition
Computer vision

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Zhu, Y., He, J., & Lawrence, R. (2012). Hierarchical modeling with tensor inputs. In Proceedings of the National Conference on Artificial Intelligence (Vol. 2, pp. 1233-1239)

Hierarchical modeling with tensor inputs. / Zhu, Yada; He, Jingrui; Lawrence, Rick.

Proceedings of the National Conference on Artificial Intelligence. Vol. 2 2012. p. 1233-1239.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhu, Y, He, J & Lawrence, R 2012, Hierarchical modeling with tensor inputs. in Proceedings of the National Conference on Artificial Intelligence. vol. 2, pp. 1233-1239, 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12, Toronto, ON, Canada, 7/22/12.
Zhu Y, He J, Lawrence R. Hierarchical modeling with tensor inputs. In Proceedings of the National Conference on Artificial Intelligence. Vol. 2. 2012. p. 1233-1239
Zhu, Yada ; He, Jingrui ; Lawrence, Rick. / Hierarchical modeling with tensor inputs. Proceedings of the National Conference on Artificial Intelligence. Vol. 2 2012. pp. 1233-1239
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