PatternRecognition_Matlab

Abstract

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).

Conclusion

Our experiments showed that SVM was the most robust method to increase dimensional space, and that SVM and LDA were the most sensitive to noise.

Documentations

Preprint report

Cite our paper

@article
{li2016comparison,
  title={Comparison of Feature Reduction Approaches and Classification Approaches for Pattern Recognition},
  author={Li, Xiaoyang},
  journal={Available at SSRN 3659735},
  year={2016}
}

Code Run Instruction

Input data : data

Main function : mainFCT.m

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