CMF {CMF} | R Documentation |
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
.
CMF(X, inds, K, likelihood, D, test = NULL, opts = NULL)
X |
List of input matrices. |
inds |
A length(X) times 2 matrix that links dimensions of the matrices in X to object sets. inds[m, 1] tells which object set corresponds to the rows in matrix X[[m]], and inds[m, 2] tells the same for the columns. |
K |
The number of factors. |
opts |
A list of options as given by getCMFopts(). If set to NULL, the default values will be used. |
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
|
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 Newton-Raphson method, whereas the rest use closed-form 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.
A list of
U |
A list of the mean parameters for the rank-K projection matrices, one for each object set. |
covU |
A list of the variance parameters for the rank-K 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
root-mean-square 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 Uall matrix. Each part of the list contains a vector that has the numbers for each particular object set. |
out |
If test matrices were provided, returns the reconstructed data sets. Otherwise returns NULL. |
M |
The number of input matrices. |
likelihood |
The likelihoods of the matrices. |
opts |
The options used for running the code. |
Arto Klami and Lauri Väre
Arto Klami, Guillaume Bouchard, and Abhishek Tripathi. Group-sparse embeddings in collective matrix factorization. arXiv:1312.5921, 2013.
#See CMF-package for an example.