[mlpack-svn] [MLPACK] #345: Sparse Autoencoder Module

[MLPACK Trac]

#345: Sparse Autoencoder Module
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  Reporter:  siddharth.950  |        Owner:     
      Type:  enhancement    |       Status:  new
  Priority:  major          |    Milestone:     
 Component:  mlpack         |   Resolution:     
  Keywords:                 |     Blocking:     
Blocked By:                 |  
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Comment (by rcurtin):

 Yeah -- there is a sparsity penalty parameter, but unless I have
 misunderstood it does not actually force the activations to be 0, just
 small.  What I mean is that you end up with activations that are like
 1e-10, but not 0.  The model, as you have it implemented, will represent
 them as 1e-10; then, when the model is used, all of the connections
 between the hidden layer and the visible layer are evaluated.  If it was
 truly sparse, many of those connections could be ignored, and execution
 could probably be faster.  That's an idea for another day, though.

 However... it does beg the question (and I'm not sure how I overlooked
 this before): you've implemented support for training a sparse
 autoencoder, but what will a user be able to do with this object once it's
 trained?  I don't see any functions that would allow the user to actually
 utilize the result to make predictions or anything like that.

-- 
Ticket URL: <http://trac.research.cc.gatech.edu/fastlab/ticket/345#comment:10>
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MLPACK is an intuitive, fast, and scalable C++ machine learning library developed at Georgia Tech.