Classic PCA algorithm detailed flow
2016-08-23
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Programming steps:
1, take the mean
2. Calculate the covariance matrix and its eigenvalues and eigenvectors
3. Calculate the eigenvalues of the covariance matrix is greater than a threshold number of
4, in descending order of eigenvalues
5, remove the smaller eigenvalue
6, to remove the larger eigenvalue (this step is not generally)
7, the combined value of the selected feature
8. Select the appropriate eigenvalues and eigenvectors
9, whitening matrix calculation
10
1, take the mean
2. Calculate the covariance matrix and its eigenvalues and eigenvectors
3. Calculate the eigenvalues of the covariance matrix is greater than a threshold number of
4, in descending order of eigenvalues
5, remove the smaller eigenvalue
6, to remove the larger eigenvalue (this step is not generally)
7, the combined value of the selected feature
8. Select the appropriate eigenvalues and eigenvectors
9, whitening matrix calculation
10
matlab
经典
pca
算法
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