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Kpca Matlab ⭐ 116

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

Kpcamnet ⭐ 57

[IEEE TCYB 2021] Official Python implementation for Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network

Patternrecognition_matlab ⭐ 39

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 classification algorithm are implemented: Support Vector Machine (SVM), Gaussian Quadratic Maximum Likelihood and K-nearest neighbors (KNN) and Gaussian Mixt

Dimensionality Reduction Using Kpca ⭐ 6

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

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