I am happy to announce that the Sparse PCA code has been reviewed and
merged into the main scikits.learn repository.
You can use it if you install the bleeding edge scikits.learn git
version, by first downloading the source code as explained in the
user’s guide, and then running python setup.py install.
[caption id=”” align=”aligncenter” width=”400” caption=”Sparse PCA on
images of the digit 3”][
][/caption]
To see what code is needed to produce an image such as the one above,
using scikits.learn. check out this cool decomposition example
that compares the results of most matrix decomposition models
implemented at the moment.
There are other new cool things that have been recently merged by other contributors, such as support for sparse matrices in minibatch K-means, and the variational infinite gaussian mixture model, so I invite you to take a look!
[]: http://scikit-learn.sourceforge.net/dev/_images/plot_digits_decomposition_4.png
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