MATLAB code for dimensionality reduction, feature extraction, fault detection, and fault diagnosis using Kernel Principal Component Analysis (KPCA).

Feature reduction projections and classifier models are learned by training dataset and applied to classify testing dataset. A few approaches of feature reduction have been compared in this paper: principle component analysis (PCA), linear discriminant analysis (LDA) and their kernel methods (KPCA,KLDA). Correspondingly, a few approaches of clas…

Application of PCA and KPCA algorithms to perform dimensionality reduction on the set of parameters in LPV models

Anomaly detection on a production line using principal component analysis (PCA) and kernel principal component analysis (KPCA) *from scratch*.