Journal article
Gaussian Process Latent Variable Model-Based Multi-Output Modeling of Incomplete Data
IEEE transactions on automation science and engineering, Vol.21(2), pp.1941-1951
04/2024
DOI: 10.1109/TASE.2023.3251386
Abstract
The rapid development of sensor technologies allows the acquisition of high dimensional sensing data. Multi-output modeling techniques have been developed to leverage the data for decision making. However, the data often contain segments of missing values, which cause great information loss and thus affect the modeling performance. This study explores the missing pattern and the correlation structure of missing segments and maximally exploits useful information in the data to improve multi-output modeling accuracy. Specifically, a new multi-output modeling method is developed based on Gaussian Process Latent Variable Model (GPLVM). A decision score is developed to seek an optimal modeling strategy and then a tailored Expectation-Maximization (EM) algorithm based on GPLVM is designed to estimate the missing segments while optimizing model parameters. The proposed method demonstrates superior performance in both a simulation study and a case study, which makes it a powerful tool to enable process automation.
Details
- Title: Subtitle
- Gaussian Process Latent Variable Model-Based Multi-Output Modeling of Incomplete Data
- Creators
- Zhiyong Hu - University of Science and Technology of ChinaChao Wang - University of IowaJianguo Wu - Peking UniversityDongping Du - Texas Tech University
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on automation science and engineering, Vol.21(2), pp.1941-1951
- Publisher
- IEEE
- DOI
- 10.1109/TASE.2023.3251386
- ISSN
- 1545-5955
- eISSN
- 1558-3783
- Number of pages
- 11
- Grant note
- CMMI-1728338 / National Science Foundation; National Science Foundation (NSF) 71932006; 72171003 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Language
- English
- Electronic publication date
- 03/07/2023
- Date published
- 04/2024
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984385051402771
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