Description: Principal Component Analysis (PCA) is based on multivariate statistical analysis of the data reduction method, which uses the correlation between process variables and establish the normal condition of the main element model, by testing samples of the new data relative to the principal component model departure from, and thus abnormal and failure.
- [Matlaboperations.Rar] - a very good operation, including linear
- [pca] - Matlab PCA on a simple example of refere
- [kmeans] - K-means image segmentation, read full-co
- [PCA-matlab] - Which contains the principal component a
- [HotellingT2] - a intricate function to compute Hotellin
- [pca-kpca] - kca & kpca matlab source code need to
- [LDA_zq] - For feature reduction, feature fusion, c
- [LSSVMNARX] - program based on LS-SVM NARX for diagnos
- [work] - The algorithm of the ICA, PCA in process
- [Statistic] - Data-driven statistical learning methods
File list (Check if you may need any files):
PCAxmeas_fault1.m
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