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Table 6 Summary of the IPCA algorithm.

From: Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets

Algorithm Principal Component Analysis with Independent loadings (IPCA)
1. Implement SVD on the centered data matrix X to generate the whitened loading vectors V, and choose the number of components m to reduce the dimension.
2. Implement FastICA on the loading vectors V and obtain the independent loading vectors ST.
3. Project the centered data matrix X on the m independent loading vectors s j and get the Independent PCs u ̃ j ,j=1...m.
4. Order the IPCs by the kurtosis value of their corresponding independent loading vectors.