Sparse Kernel Principal Component Analysis via Sequential Approach for Nonlinear Process Monitoring

Sparse Kernel Principal Component Analysis via Sequential Approach for Nonlinear Process Monitoring

Kernel principal component analysis (KPCA) has been widely used for nonlinear process monitoring. However, since the principal components are linear combinations of all kernel functions, traditional KPCA suffers from poor interpretation and high-computation cost. To address this problem, obtaining s...

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Bibliographic Details
Main Authors: Lingling Guo, Ping Wu, Jinfeng Gao, Siwei Lou
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Process monitoring
kernel-based methods
principal component analysis
sparsity
sequential approach
Online Access:https://ieeexplore.ieee.org/document/8684841/

Internet

https://ieeexplore.ieee.org/document/8684841/

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