You can also see the kmeans version it here: https://github.com/kastnerkyle/ift6268h15/blob/master/hw3/color_kmeans_theano.py#L23
Though I guarantee nothing about my homework code! On Thu, Jun 18, 2015 at 10:09 AM, Kyle Kastner <kastnerk...@gmail.com> wrote: > I don't know if it is faster or better - but the learning rule is insanely > simple and it is hard to believe there could be *anything* much faster. It > is ten lines - won't copy it here cause the license is longer than the > implementation! > > Given the connection between PCA and K-means, this implementation > (Matlab...) is also related > http://homepages.cae.wisc.edu/~ece539/matlab/ghafun.m > > This points to this paper: > http://courses.cs.washington.edu/courses/cse528/09sp/sanger_pca_nn.pdf > > Basically this is the neural net approach to K-means. I have asked if > there is a paper ref - though it might be "too easy" to have a real paper. > > > On Thu, Jun 18, 2015 at 9:58 AM, Andreas Mueller <t3k...@gmail.com> wrote: > >> >> >> On 06/18/2015 09:48 AM, Kyle Kastner wrote: >> > This link should work http://www.cs.toronto.edu/~rfm/code.html >> > <http://www.cs.toronto.edu/%7Erfm/code.html> >> Is that faster / better than minibatch k-means? Is there a paper? >> >> >> ------------------------------------------------------------------------------ >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> > >
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