acmtf_opt: Advanced coupled matrix and tensor factorizations

In CMTFtoolbox: Create (Advanced) Coupled Matrix and Tensor Factorization Models

Advanced coupled matrix and tensor factorizations

Description

Advanced coupled matrix and tensor factorizations

Usage

acmtf_opt(
  Z,
  numComponents,
  initialization = "random",
  alpha = 1,
  beta = rep(0.001, length(Z$object)),
  epsilon = 1e-08,
  method = "CG",
  cg_update = "HS",
  line_search = "MT",
  max_iter = 10000,
  max_fn = 10000,
  abs_tol = 1e-10,
  rel_tol = 1e-10,
  grad_tol = 1e-10,
  nstart = 1,
  numCores = 1,
  sortComponents = TRUE,
  allOutput = FALSE
)

Arguments

Z	Combined dataset and mode object as produced by setupCMTFdata().
numComponents	Number of components
initialization	Initialization, either "random" (default) or "nvec" for numComponents components of the concatenated data using svd.
alpha	Scalar penalizing the components to be norm 1 (default 1).
beta	Vector of penalty values for each dataset, penalizing the lambda terms (default 1e-3).
epsilon	Scalar value to make it possible to compute the partial derivatives of lambda (default 1e-8).
method	Optimization method to use (default = "CG", the conjugate gradient). See mize::mize() for other options.
cg_update	Update method for the conjugate gradient algorithm, see mize::mize() for the options (default="HS", Hestenes-Steifel).
line_search	Line search algorithm to use, see mize::mize() for the options (default="MT", More-Thuente).
max_iter	Maximum number of iterations.
max_fn	Maximum number of function evaluations.
abs_tol	Function tolerance criterion for convergence.
rel_tol	Relative function tolerance criterion for convergence.
grad_tol	Absolute tolerence for the l2-norm of the gradient vector.
nstart	Number of models to produce (default 1). If set higher than one, the package will return the best fitted model.
numCores	Number of cores to use (default 1). If set higher than one, the package will attempt to run in parallel.
sortComponents	Sort the components in the output by descending order of variation explained.
allOutput	Return all created models. Ignored if nstart=1.

Value

List object, similar to mize::mize() output. Includes a Fac object of the model, which is a list of components per mode. Also includes an init object giving the initialized input vectors.

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

# specific setting to reduce runtime for CRAN
model = acmtf_opt(Z, 1, rel_tol=1e-5, abs_tol=1e-5)

CMTFtoolbox documentation built on Aug. 23, 2025, 1:11 a.m.