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From prasenjit mukherjee <>
Subject Re: Using Restricted Boltzmann for clustering
Date Thu, 03 Dec 2009 03:14:54 GMT
I am using 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.


On Thu, Dec 3, 2009 at 12:40 AM, Olivier Grisel
<> wrote:
> 2009/12/2 Jake Mannix <>:
>> 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:
> 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
> -

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