Robust PCA multivariate control chart mainly consists two steps: Step1 Calculates the robust mean and the robust covariance of original dataset using the minimum covariance determinant (MCD). In MCD technique, finding a subset containing half of the data such that its covariance matrix has the lowest determinant, then using this subset to calculate the robust mean and the robust covariance matrix (Hubert, Rousseeuw, & Branden, 2005) Step2 Standardize data using robust mean and robust standard deviation from Step1. Apply PCA analysis, calculate the principalcomponent score matrix Y=ZA, where Z is the robust standardized data matrix, and A is p*p matrix of eigenvectors (also called principalcomponents)

Matlab codes for RobustPCA.txt

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