CMF {CMF}  R Documentation 
Collective Matrix Factorization
Description
Learns the CMF model for a given collection of M matrices.
The code learns the parameters of a variational approximation for CMF,
and also computes predictions for indices specified in test
.
Usage
CMF(X, inds, K, likelihood, D, test = NULL, opts = NULL)
Arguments
X 
List of input matrices. 
inds 
A 
K 
The number of factors. 
likelihood 
A list of likelihood choices, one for each matrix in X. Each entry should be a string with possible values of: "gaussian", "bernoulli" or "poisson". 
D 
A vector containing sizes of each object set. 
test 
A list of test matrices. If not NULL, the code will compute
predictions for these elements of the matrices. This duplicates
the functionality of 
opts 
A list of options as given by 
Details
The variational approximation is fully factorized over all of the model parameters, including individual elements of the projection matrices. The parameters for the projection matrices are updated jointly by NewtonRaphson method, whereas the rest use closedform updates.
Note that the input data needs to be given in a specific sparse format.
See matrix_to_triplets()
for details.
The behavior of the algorithm can be modified via the opts
parameter.
See getCMFopts()
for details. Of particular interest are the elements
useBias
and method
.
For full description of the output parameters, see the referred publication. The notation in the code follows roughly the notation used in the paper.
Value
A list of
U 
A list of the mean parameters for the rankK projection matrices, one for each object set. 
covU 
A list of the variance parameters for the rankK projection matrices, one for each object set. 
tau 
A vector of the precision parameter means. 
alpha 
A vector of the ARD parameter means. 
cost 
A vector of variational lower bound values. 
inds 
The input parameter 
errors 
A vector containing rootmeansquare errors for each
iteration, computed over the elements indicated by the

bias 
A list (of lists) storing the parameters of the row and column bias terms. 
D 
The sizes of the object sets as given in the parameters. 
K 
The number of components as given in the parameters. 
Uall 
Matrices of U joined into one sum(D) by K matrix, for easier plotting of the results. 
items 
A list containing the running number for each item among
all object sets. This corresponds to rows of the 
out 
If test matrices were provided, returns the reconstructed data
sets. Otherwise returns 
M 
The number of input matrices. 
likelihood 
The likelihoods of the matrices. 
opts 
The options used for running the code. 
Author(s)
Arto Klami and Lauri VĂ¤re
References
Arto Klami, Guillaume Bouchard, and Abhishek Tripathi. Groupsparse embeddings in collective matrix factorization. arXiv:1312.5921, 2014.
Examples
# See CMFpackage for an example.