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 group-wise sparse CMF that identifies both
shared and private factors (see Klami et al. (2013) for
details). The value "CMF"
turns off the group-wise
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 multi-view 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 under-relaxed Newton iterations. 0=no regularization, larger values provide inncreasing 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 one. |
grad.max |
Maximum absolute change for the elements of the projection matrices during one gradient step. Small values help to prevent over-shooting, wheres inf results to no constraints. Default is inf. |
iter.max |
Number of iterations for the whole algorithm. |
computeCost |
Should the cost function values be computed or not. Defaults to TRUE. |
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 FALSE to exclude the row and column bias terms. The default is TRUE. |
method
|
Default value of "gCMF" computes the CMF with group-sparsity. The other possible values are "CMF" for turning off the group-sparsity prior, and "GFA" for implementing group factor analysis (and canonical correlation analysis when M=2). |
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 Väre
Arto Klami, Guillaume Bouchard, and Abhishek Tripathi. Group-sparse embeddings in collective matrix factorization. arXiv:1312.5921, 2013.
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 1269-1277, 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 #Don't take row and column means into consideration. #These options will be in effect when CMF_options is passed on to CMF.