[mlpack-svn] [MLPACK] #363: use Armadillo diagonal GMM implementation when DiagonalConstraint is used with GMMs

[MLPACK Trac]

#363: use Armadillo diagonal GMM implementation when DiagonalConstraint is used
with GMMs
----------------------+-----------------------------------------------------
 Reporter:  rcurtin   |        Owner:                                              
     Type:  wishlist  |       Status:  new                                         
 Priority:  trivial   |    Milestone:                                              
Component:  mlpack    |     Keywords:  gmm, diagonal gmm, emfit, diagonalconstraint
 Blocking:            |   Blocked By:                                              
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 The mlpack implementation of GMMs is hilariously inefficient.  A user will
 write this code:

 {{{
 GMM<EMFit<KMeans, DiagonalConstraint> > gmm(dataset);
 }}}

 but what this code will actually do is run the regular full-covariance EM
 algorithm, and then set all non-diagonal elements of each covariance
 matrix to 0.  This is obviously not the right way to do it.

 So the right thing to do is probably to specialize EMFit<...,
 DiagonalConstraint> so that it uses an algorithm specifically for diagonal
 covariances and not the entire covariance matrix.

 Armadillo now contains an implementation of diagonal GMMs, which might be
 useful for this specialization.  If it will be used, one should ensure
 that the amount of memory being copied is minimal.

-- 
Ticket URL: <https://trac.research.cc.gatech.edu/fastlab/ticket/363>
MLPACK <www.fast-lab.org>
MLPACK is an intuitive, fast, and scalable C++ machine learning library developed at Georgia Tech.