My manifold learning code was for some time a Technology Preview in the scikit learn. Now I can say that it is available (BSD license) and there should not be any obvious bug left..

I’ve written a small tutorial. It is not an usual tutorial (there is a user tutorial and then what developers should know to enhance it), and some results of the techniques are exposed in my blog. It provides the basic commands to start using the scikit yourself (reducing some data, projecting new points, …) as well as the expoed interface to enhance the scikit.

If you have any question, feel free to ask me, I will add the answers to the tutorial page so that everyone can benefit from it.

Be free to contribute new techniques and additional tools as well, I cannot write them all ! For instance, the scikit lacks some robust neighbors selection to avoid short-cuts in the manifold…

Tutorial and the learn scikit mainpage.

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At last, my article on manifold learning has been published and is accessible with doi.org (it was not the case last week, that’s why I waited before publishing this post).
The journal is free, so you won’t have to pay to read it : Access to the EURASIP JASP article

I will publish additional figures here in a short time. The scikit is almost completed as well, I’m finishing the online tutorial for those who are interested in using it and/or enhancing it.

Today, I’m publishing a tutorial on two C++ profilers on my French website. The question I’m asking myself and you is: should I translate it ?

If some of you are interested in my French tutorials, I may translate them from time to time, depending on their content (I don’t want to translate an article on Boost for instance, the documentation does provide everything). But I’ll do that only if people tell me “Go on”. So I’m all ears…