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).

Xiaoyang-Rebecca
Xiaoyang-Rebecca
Date Created 2017-10-18 (6 years ago)
Commits 12 (last one 3 years ago)
Stargazers 69 (0 this week)
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Homepage URL: https://www.researchgate.net/publication/308927930_Comparison_of_Feature_Reduction_Approaches_and_Classification_Approaches_for_Pattern_Recognition

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updated: 2024-06-21 @ 04:45pm, id: 107423607 / R_kgDOBmcndw