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Patternrecognition_matlab
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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
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6
Application of PCA and KPCA algorithms to perform dimensionality reduction on the set of parameters in LPV models
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