Statistical transfer learning: A review and some extensions to statistical process control

ABSTRACT

The rapid development of information technology, together with advances in sensory and data acquisition techniques, has led to the increasing necessity of handling datasets from multiple domains. In recent years, transfer learning has emerged as an effective framework for tackling related tasks in target domains by transferring previously-acquired knowledge from source domains. Statistical models and methodologies are widely involved in transfer learning and play a critical role, which, however, has not been emphasized in most surveys of transfer learning. In this article, we conduct a comprehensive literature review on statistical transfer learning, i.e., transfer learning techniques with a focus on statistical models and statistical methodologies, demonstrating how statistics can be used in transfer learning. In addition, we highlight opportunities for the use of statistical transfer learning to improve statistical process control and quality control. Several potential future issues in statistical transfer learning are discussed.

KEYWORDS:

• 3D printing
• bayesian modeling
• landslides
• quality control
• regularization
• statistical process control
• statistical transfer learning
• transfer learning
• urban rail transit

Acknowledgments

The authors are grateful to professor Steven Rigdon, professor Xiaoming Huo and the two anonymous reviewers for their comments and suggestions that greatly improved our article.

Funding

Professor Tsung's research was supported by the RGC GRF 16203917.

Additional information

Notes on contributors

Fugee Tsung

Fugee Tsung is Professor of the Department of Industrial Engineering and Logistics Management (IELM), Director of the Quality and Data Analytics Lab, at the Hong Kong University of Science and Technology (HKUST). He is a Fellow of the Institute of Industrial Engineers (IIE), Fellow of the American Society for Quality (ASQ), Fellow of the American Statistical Association (ASA), Academician of the International Academy for Quality (IAQ) and Fellow of the Hong Kong Institution of Engineers (HKIE). He is Editor-in-Chief of Journal of Quality Technology (JQT), Department Editor of the IIE Transactions, and Associate Editor of Technometrics. He has authored over 100 refereed journal publications, and is the winner of the Best Paper Award for the IIE Transactions in 2003 and 2009. He received both his M.Sc. and Ph.D. from the University of Michigan, Ann Arbor and his B.Sc. from National Taiwan University. His research interests include quality engineering and management to manufacturing and service industries, statistical process control and monitoring, industrial statistics, and data analytics.

Ke Zhang

Ke Zhang is a Ph.D. candidate in Department of Industrial Engineering and Logistics Management at Hong Kong University of Science and Technology. He received a Bachelor's degree in Statistics from University of Science and Technology of China in 2014. His research interests include statistical modeling, process control and data mining.

Longwei Cheng

Longwei Cheng is a Ph.D. candidate in Department of Industrial Engineering and Logistics Management at Hong Kong University of Science and Technology. He received a Bachelor's degree in Automation from University of Science and Technology of China in 2014. His research interests include statistical modeling and quality control.

Zhenli Song

Zhenli Song is a Ph.D. candidate in Department of Industrial Engineering and Logistics Management at Hong Kong University of Science and Technology. He received a Bachelor's degree in Statistics from University of Science and Technology of China in 2015. His research interests include statistical modeling and data mining.