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Gaussian Process Latent Variable Model-Based Multi-Output Modeling of Incomplete Data
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AI
DOI：10.1109/TASE.2023.3251386.png)
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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.
Keyword:
Data models
Correlation
Gaussian processes
Sensors
Load modeling
Training
Stress
Gaussian process latent variable model
multi-output modeling
missing data
expectation maximization
期刊
IF:
6.4
论文数:
5.1K
被引数:
1.6W
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