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).

Date Created 2017-10-18 (7 years ago)

Commits 12 (last one 3 years ago)

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

All Topics

gmm lda pca svm kpca pattern-recognition gaussian-mixture-models

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updated: 2025-01-15 @ 09:17am, id: 107423607 / R_kgDOBmcndw