mabounassif - 3 months ago 82x

Python Question

I'm working with python and I've implemented the PCA using the following tutorial http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf

Everything works great, I got the Covariance I did a successful transform, brought it make to the original dimensions not problem.

But how do I perform whitening? I tried dividing the eigenvectors by the eigenvalues:

S, V = numpy.linalg.eig(cov)

V = V / S[:, numpy.newaxis]

and used V to transform the data but this led to weird data values.

Could someone please shred some light on this?

Answer

Here's a numpy implementation of some Matlab code for matrix whitening I got from here.

```
import numpy as np
def whiten(X,fudge=1E-18):
# the matrix X should be observations-by-components
# get the covariance matrix
Xcov = np.dot(X.T,X)
# eigenvalue decomposition of the covariance matrix
d, V = np.linalg.eigh(Xcov)
# a fudge factor can be used so that eigenvectors associated with
# small eigenvalues do not get overamplified.
D = np.diag(1. / np.sqrt(d+fudge))
# whitening matrix
W = np.dot(np.dot(V, D), V.T)
# multiply by the whitening matrix
X_white = np.dot(X, W)
return X_white, W
```

You can also whiten a matrix using SVD:

```
def svd_whiten(X):
U, s, Vt = np.linalg.svd(X)
# U and Vt are the singular matrices, and s contains the singular values.
# Since the rows of both U and Vt are orthonormal vectors, then U * Vt
# will be white
X_white = np.dot(U, Vt)
return X_white
```

The second way is a bit slower, but probably more numerically stable.

Source (Stackoverflow)

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