Another citation for hebbian approach - it is related to this http://onlinelibrary.wiley.com/doi/10.1207/s15516709cog0901_5/pdf
On Thu, Jun 18, 2015 at 10:25 AM, Kyle Kastner <kastnerk...@gmail.com> wrote: > Yes agreed - though I would also guess the intermediate memory blowup > could help speed, though I haven't tested. I guess it comes back to the > question, has anyone done MiniBatchKMeans on MNIST? To be honest I don't > recall the original the question from ~1 year ago, but would be very > surprised if there was a problem. I use MiniBatchKMeans (and this hebbian > thing) on *much* larger datasets... > > On Thu, Jun 18, 2015 at 10:17 AM, Andreas Mueller <t3k...@gmail.com> > wrote: > >> You could implement Lloyds algorithm in as little code, too. >> One of the reasons that the sklearn implementation is much longer is that >> it doesn't do fancy indexing and avoids large intermediate arrays. >> >> >> >> On 06/18/2015 10:09 AM, Kyle Kastner 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 >>> >> >> >> >> ------------------------------------------------------------------------------ >> >> >> >> _______________________________________________ >> Scikit-learn-general mailing >> listScikit-learn-general@lists.sourceforge.nethttps://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> >> >> >> ------------------------------------------------------------------------------ >> >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> >
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