fengjianshe / Kernel-Principal-Component-Analysis-KPCA

KPCA for dimensionality reduction, feature extraction , fault detection, and fault diagnosis

Kernel Principal Component Analysis (KPCA)

MATLAB Code for non-linear dimensionality reduction, fault detection, and fault diagnosis through the use of kernels.

Version 2.1, 6-MAY-2020

Email: [e-mail]

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Main features

• Easy-used API for training and testing KPCA model
• Multiple kinds of kernel functions
• Support for dimensionality reduction, fault detection, and fault diagnosis
• Support for data reconstruction

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Notices

• Only fault diagnosis of Gaussian kernel is supported.
• Class is defined using 'Classdef...End', so this code can only be applied to MATLAB after the R2008a release.
• More details and discussions please see: https://www.ilovematlab.cn/thread-560380-1-1.html
• This code is for reference only.

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Demo for dimensionality reduction ('banana' data and 'circle' data)

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Demo for data reconstruction ('circle' data)

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Demo for fault detection (TE process data)

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Demo for fault diagnosis (TE process data)