GitHub / Xiaoyang-Rebecca / 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).

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PURL: pkg:github/Xiaoyang-Rebecca/PatternRecognition_Matlab

Topics: gaussian-mixture-models, gmm, kpca, lda, pattern-recognition, pca, svm

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