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

Table 6 Summary of the IPCA algorithm.

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 S^T.
3. Project the centered data matrix X on the m independent loading vectors s_j and get the Independent PCs ${\tilde{u}}_{j}, j = 1 . . . m$ .
4. Order the IPCs by the kurtosis value of their corresponding independent loading vectors.

ISSN: 1471-2105