I am happy to announce that the Sparse PCA code has been reviewed and
merged into the main
You can use it if you install the bleeding edge
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!