I am working on implementing the Orthogonal Matching Pursuit (OMP)
algorithm for the scikit. It is an elegant algorithm (that almost writes
itself in Numpy!) to compute a greedy approximation to the solution of a
sparse coding problem:

\$\$ \text{argmin} \big|\big|\gamma\big|\big|_0 \text{ subject
to }\big …

I have been working on the integration into the scikits.learn codebase
of a sparse principal components analysis (SparsePCA) algorithm coded by
Gaël and Alexandre and based on [[1]][]. Because the name “sparse PCA”
has some inherent ambiguity, I will describe in greater depth what
problem we are actually solving …

My GSoC proposal is titled “Dictionary learning in scikits.learn” and in
the project, I plan to implement methods used in state of the art
research and industry applications in signal and image processing. In
this post, I want to clarify the terminology used.