# Principal Components - how to obtain linear transformations?

I have a list "xlsf" with 6 columns and 1200 rows for PCA analysis. The PrincipalComponents[xlsf] gives the following:

"The principal components of matrix are linear transformations of the original columns into uncorrelated columns arranged in order of decreasing variance"

How do I obtain the transformations performed on columns by Mathematica?

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Use SingularValueDecomposition. –  Szabolcs Jun 17 at 16:02
Or KarhunenLoeveDecomposition –  Oleksandr R. Jun 17 at 18:59

PrincipalComponents is based on SingularValueDecomposition. Below I'll show what it does:

This is some sample data from the docs:

data = {{13.2, 200, 58, 21.2}, {10, 263, 48, 44.5}, {8.1, 294, 80,
31}, {8.8, 190, 50, 19.5}, {9, 276, 91, 40.6}, {7.9, 204, 78,
38.7}, {3.3, 110, 77, 11.1}, {5.9, 238, 72, 15.8}, {15.4, 335,
80, 31.9}, {17.4, 211, 60, 25.8}};


Let's centre it first:

data2 = # - Mean[data] & /@ data;


Then compute the SVD of the centred data:

{u, s, v} = SingularValueDecomposition[data2];


Now Transpose[v] is the rotation that PrincipalComponents applies to each row.

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what happens if I do not center the data? –  denfromufa Jun 17 at 21:49
@denfromufa Do you understand the mathematics behind PCA? –  Szabolcs Jun 17 at 21:55
now I understand that PCA can be obtained by doing SVD to centralized data: math.stackexchange.com/questions/3869/… –  denfromufa Jun 18 at 22:46