Local and Global Randomized Principal Component Analysis for Nonlinear Process Monitoring
Kernel principal component analysis (KPCA) has been widely used in nonlinear process monitoring since it can capture the nonlinear process characteristics. However, it suffers from high computational complexity and poor scalability while dealing with real-time process monitoring and large-scale proc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8649617/ |