Progress on Orthogonal Matching Pursuit

Posted on August 2, 2011


Since orthogonal matching pursuit (OMP) is an important part of signal processing and therefore crucial to the image processing aspect of dictionary learning, I am currently focusing on optimizing the OMP code and making sure it is stable. OMP is a forward method like least-angle regression, so it is natural to bench them against one another.

This has helped find a couple of bottlenecks. Time has been gained by preallocating the array to store the Cholesky decomposition. Also, using the LAPACK potrs function in order to solve a system of the shape LL'x=y is faster than using solve_triangular twice.

I am still trying to optimize the code. We are working hard to make sure that scikits.learn contributions are up to standards before merging.

Posted in: scikits.learn