This link should work http://www.cs.toronto.edu/~rfm/code.html
On Thu, Jun 18, 2015 at 9:38 AM, Kyle Kastner <kastnerk...@gmail.com> wrote: > Minibatch K-means should work just fine. Alternatively there are hebbian > K-means approaches which are quite easy to implement and should be fast > (though I think it basically boils down to minibatch K-means, I haven't > looked at details of minibatch K-means). There is an approach here > http://www.iro.umontreal.ca/~memisevr/code.html that could be useful once > the website is fixed... > > I have run the hebbian K-means approach over CIFAR10, so it should work > for MNIST. > > On Thu, Jun 18, 2015 at 8:47 AM, Vince Fernando <y...@vincefernando.co.uk> > wrote: > >> What is best routine in scikit-learn (or elsewhere) for clustering large >> data sets such as MNIST? >> I asked a similar question last year but would like to hear an update. >> >> >> ------------------------------------------------------------------------------ >> >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> >
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