通过训练数据集学习特征约简预测和分类器模型，并应用于测试数据集的分类.zip

# 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 <p align="center"><img src="./figures/fselection.png" width="600" class="inline"/></p> 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](https://www.researchgate.net/publication/308927930_Comparison_of_Feature_Reduction_Approaches_and_Classification_Approaches_for_Pattern_Recognition) ## 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](./data.mat) Main function : [mainFCT.m](./mainFCT.m) ## About author [Porfolios](https://xiaoyang-rebecca.github.io)