文件名称:Matlabcodes-RobustPCA
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- 数学计算/工程计算
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- 2012-11-26
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Matlab codes for Robust PCA multivariate control chart-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)
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)
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Matlab codes for RobustPCA.txt