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PatternRecognition_Matlab

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 Mixture Model(GMM).

See more at

https://www.researchgate.net/publication/308927930_Comparison_of_Feature_Reduction_Approaches_and_Classification_Approaches_for_Pattern_Recognition

Main function is mainFCT.m