getCMFopts {CMF}  R Documentation 
A helper function that creates a list of options to be passed
to CMF
. To run the code with other option values, first
run this function and then directly modify the entries before
passing the list to CMF
.
getCMFopts()
Most of the parameters are for controlling the optimization, but some will
alter the model itself. In particular, useBias
is used for turning
the bias terms on and off, and method
will change the prior for
U
.
The default choice for method
is "gCMF"
, providing the
groupwise sparse CMF that identifies both shared and private factors
(see Klami et al. (2013) for details). The value "CMF"
turns off
the groupwise sparsity, providing a CMF solution that attempts to learn
only factors shared by all matrices. Finally, method="GFA"
implements
the group factor analysis (GFA) method, by fixing the variance of
U[[1]]
to one and forcing useBias=FALSE
. Then U[[1]]
can be interpreted as latent variables with unit variance and zero mean,
as assumed by GFA and CCA (special case of GFA with M=2). Note that as a
multiview learning method "GFA"
requires all matrices to share the
same rows, the very first entity set.
Returns a list of:
init.tau 
Initial value for the noise precisions. Only matters for Gaussian likelihood. 
init.alpha 
Initial value for the automatic relevance determination (ARD) prior precisions. 
grad.reg 
The regularization parameter for the underrelaxed Newton iterations. 0 = no regularization, larger values provide increasing regularization. The value must be below 1. 
gradIter 
How many gradient steps for updating the projections are performed during each iteration of the whole algorithm. Default is 1. 
grad.max 
Maximum absolute change for the elements of the projection
matrices during one gradient step. Small values help to
prevent overshooting, wheres inf results to no constraints.
Default is 
iter.max 
Number of iterations for the whole algorithm. 
computeCost 
Should the cost function values be computed or not.
Defaults to 
verbose 
0 = supress all printing, 1 = print current iteration and test RMSE every now and then, 2 = in addition to level 1 print also the current gradient norm. 
useBias 
Set this to 
method 
Default value of "gCMF" computes the CMF with groupsparsity.
The other possible values are "CMF" for turning off the
groupsparsity prior, and "GFA" for implementing group factor
analysis (and canonical correlation analysis when

prior.alpha_0 
Hyperprior values for the gamma prior for ARD. 
prior.alpha_0t 
Hyperprior values for the gamma prior for tau. 
Arto Klami and Lauri VĂ¤re
Arto Klami, Guillaume Bouchard, and Abhishek Tripathi. Groupsparse embeddings in collective matrix factorization. arXiv:1312.5921, 2014.
Seppo Virtanen, Arto Klami, Suleiman A. Khan, and Samuel Kaski. Bayesian group factor analysis. In Proceedings of the 15th International Conference on Artificial Intelligence and Statistics, volume 22 of JMLR:W&CP, pages 12691277, 2012.
'CMF'
CMF_options = getCMFopts() CMF_options$iter.max = 500 # Change the number of iterations from default # of 200 to 500. CMF_options$useBias = FALSE # Do not take row and column means into # consideration. # These options will be in effect when CMF_options is passed on to CMF.