Monitoring Statistics and Tuning of Kernel Principal Component Analysis With Radial Basis Function Kernels

Monitoring Statistics and Tuning of Kernel Principal Component Analysis With Radial Basis Function Kernels

Kernel Principal Component Analysis (KPCA) using Radial Basis Function (RBF) kernels can capture data nonlinearity by projecting the original variable space to a high-dimensional kernel feature space and obtaining the kernel principal components. This article examines the tuning of the kernel width...

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Bibliographic Details
Main Authors: Ruomu Tan, James R. Ottewill, Nina F. Thornhill
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Anomaly detection
asymptotic analysis
fault detection
kernel principal component analysis
monitoring statistic
multivariate statistics
Online Access:https://ieeexplore.ieee.org/document/9241766/

Internet

https://ieeexplore.ieee.org/document/9241766/

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