Browsing All posts tagged under »orthogonal matching pursuit«

The nasty bug crawling in my Orthogonal Matching Pursuit code

November 18, 2011

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A while back, Bob L. Sturm blogged about a similar implementation of OMP to the one in scikit-learn. Instead of using the Cholesky decomposition like we did, his Matlab code uses the QR decomposition, to a similar (or maybe even identical) outcome, in theory. So lucky that Alejandro pointed out to him the existence of […]

Optimizing Orthogonal Matching Pursuit code in Numpy, part 2

August 11, 2011

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EDIT: There was a bug in the final version of the code presented here. It is fixed now, for its backstory, check out my blog post on it. When we last saw our hero, he was fighting with the dreaded implementation of least-angle regression, knowing full well that it was his destiny to be faster. […]

Optimizing Orthogonal Matching Pursuit code in Numpy, part 1

August 7, 2011

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After intense code optimization work, my implementation of OMP finally beat least-angle regression! This was the primary issue discussed during the pull request, so once performance was taken care of, the code was ready for merge. Orthogonal matching pursuit is now available in scikits.learn as a sparse linear regression model. OMP is a key building […]

Progress on Orthogonal Matching Pursuit

August 2, 2011

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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 […]