I am using http://sourceforge.net/projects/jarbm. Not sure how easily the algorithms can be mapreducable.
Great to hear similar view point. Although a bit too early, but it seems that the -ve weights ( in RBM ) do have a better interpretation which is not there in the SVDs. If you consider each hidden neuron as a cluster the -ve weights tend to specify the denial of a partciular cluster if that feature is present. Again, these are just some observations on a preliminary set of data, and would definitely appreciate any kind of supporting theory. -Prasen On Thu, Dec 3, 2009 at 12:40 AM, Olivier Grisel <[email protected]> wrote: > 2009/12/2 Jake Mannix <[email protected]>: >> Prasen, >> >> I was just talking about this on here last week. Yes, RBM-based >> clustering can be viewed as >> a nonlinear SVD. I'm pretty interested in your findings on this. Do you >> have any RBM code you >> care to contribute to Mahout? > > Hi, > > I have some C + python code for stacking autoencoders which share > similar features as DBN (stacked RBM) here: > http://bitbucket.org/ogrisel/libsgd/wiki/Home > > This is still pretty much work in progress, I will let you know when I > have easy to run sample demos. > > However, this algo is not trivially mapreducable but I plan to > investigate on that matters in the coming weeks. Would be nice to have > a pure JVM version too. I am also planning to play with clojure + > incanter (with the parallelcolt library as a backend for linear > algebra) to make it easier to work with Hadoop. > > -- > Olivier > http://twitter.com/ogrisel - http://code.oliviergrisel.name >
