ADD: Python code for three aproaches towards Robust PCA.

All approaches are based on a low-rank + sparse decomposition
of the data matrix. Candes et al.'s approach can be used to
actually recover the low-rank part in case of outliers. Xu et
al.'s proposal is directed more towards outlier identification,
since whole observations are allowed to be corrupted, as opposed
to randomly distributed corruptions.

ADD: pyrpca (in Base/Python/pyrpca)
ADD: Preliminary README.md file how to use the code.
ADD: Two tests using Python's unittest

Change-Id: I34b827146d7ba3410ff40df18caf522e6f8853c5
ENH: pyrpca style issues.

Change-Id: Ic06994b383809a19458f5d121fc42e1544cb7222
Merge topic 'rk-pyrpca'

48e5026 ENH: pyrpca style issues.
5a5a546 ADD: Python code for three aproaches towards Robust PCA.