Partial Co-training for virtual metrology

Published

Conference Paper

© 2017 IEEE. Virtual metrology is an important tool for industrial automation. To accurately build regression models for virtual metrology, we consider semi-supervised learning where labeled data are expensive to collect, but unlabeled data are abundant. In such a scenario, due to the scarcity of labeled data, traditional single-view learning methods face the risk of overfitting. To address the overfitting issue, we develop a Partial Co-training framework, which is an extension of the original co-training approach by means of an undirected probabilistic graphical model. Unlike other co-training techniques, this model creates a partial view by shrinking the original feature space, and makes use of this partial-view to provide guidance information for improving the complete-view model. Our approach is validated with data from two manufacturing applications. The results indicate that a consistent and robust estimation is achievable with very limited labeled data.

Full Text

Duke Authors

Cited Authors

  • Nguyen, C; Li, X; Blanton, RDS

Published Date

  • June 28, 2017

Published In

Start / End Page

  • 1 - 8

Electronic International Standard Serial Number (EISSN)

  • 1946-0759

International Standard Serial Number (ISSN)

  • 1946-0740

International Standard Book Number 13 (ISBN-13)

  • 9781509065059

Digital Object Identifier (DOI)

  • 10.1109/ETFA.2017.8247660

Citation Source

  • Scopus