PatternRecognition_Matlab
Pattern recognition study
An investigation into feature reduction and classification methods for pattern recognition using various techniques such as PCA, LDA, and SVM.
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).
71 stars
2 watching
21 forks
Language: MATLAB
last commit: over 3 years ago
Linked from 1 awesome list
gaussian-mixture-modelsgmmkpcaldapattern-recognitionpcasvm
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