I wrote a Java library implementing Principal Component Analysis data transformation. About two years ago I needed such an algorithm implemented in Java and I was not able to find one, hence this library.

The library along with a more detailed description as well as binary and source packages is placed on github: https://github.com/mkobos/pca_transform

Świetnie, właśnie tego szukałam :). Z ciekawości – Kobos z V lo?

To fajnie:) Nie, nie z V lo.

How to reconstruct the data after PCA transformation?

Below is the code I used. It is not working. Can you advise?

for (int i = 0; i < transformedData.getRowDimension(); i++) {

for (int k = 0; k < PCA_Dimension; k++) {

for (int j = 0; j < transformedData.getColumnDimension(); j++) {

restoredData[i][j] = restoredData[i][j] + transformedData.get(i, k)

* pca.getEigenvalue(k) * pca.getEigenvectorsMatrix().get(k, j);

}

}

}

I assume that you use the whitening transformation. In such case, the transformation from the original space to transformed space is Z = X V D^{-1/2}, where X is the original data matrix (data points correspond to rows), V is the eigenvectors matrix (eigenvectors correspond to columns), D is the diagonal matrix with eigenvalues on the diagonal, Z is the transformed data matrix (data points correspond to rows). The reverse transformation (that will get you the original vector only if the number of dimensions hasn’t been reduced) is X = Z D^{1/2} V^{-1} = Z D^{1/2} V^T (see e.g. http://stats.stackexchange.com/a/229093/43697 for an explanation for a version with the rotation transformation).

In that context, I can see two problems with your code: it has swapped indexes when iterating over the `pca.getEigenvectorsMatrix()` (i.e., the V matrix) and is missing square root when handling the eigenvalue.

for (int i = 0; i < transformedData.getRowDimension(); i++) {

for (int k = 0; k < pca.getOutputDimsNo(); k++) {

for (int j = 0; j < transformedData.getColumnDimension(); j++) {

restoredData[i][j] = restoredData[i][j] + transformedData.get(i, k)

* Math.sqrt(pca.getEigenvalue(k)) * pca.getEigenvectorsMatrix().get(j, k);

}

}

}

Of course this is assuming that `restoredData` matrix is initialized with zeros.