ACMTF_modelSelection: Model selection for ACMTF

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

Model selection for ACMTF

Description

Model selection for ACMTF

Usage

ACMTF_modelSelection(
  datasets,
  modes,
  maxNumComponents = 3,
  sharedMode = 1,
  alpha = 1,
  beta = rep(0.001, length(datasets)),
  epsilon = 1e-08,
  nstart = 10,
  cvFolds = 10,
  numCores = 1,
  method = "CG",
  cg_update = "HS",
  line_search = "MT",
  max_iter = 10000,
  max_fn = 1e+05,
  rel_tol = 1e-08,
  abs_tol = 1e-08,
  grad_tol = 1e-08,
  plots = TRUE
)

Arguments

datasets	List of arrays of datasets. Multi-way and two-way may be combined.
modes	Numbered modes per dataset in a list. Example element 1: 1 2 3 and element 2: 1 4 for the X tensor and Y matrix case with a shared subject mode.
maxNumComponents	Maximum number of components to check (default 3).
sharedMode	Mode that is shared between all blocks, used to remove fibers for numFolds randomly initialized models.
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).
nstart	Number of randomly initialized models to create (default 10).
cvFolds	Number of CV folds to create (default 10).
numCores	Number of cores to use (default 1). A number higher than 1 will run the process in parallel.
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.
rel_tol	Relative function tolerance criterion for convergence.
abs_tol	Function tolerance criterion for convergence.
grad_tol	Absolute tolerence for the l2-norm of the gradient vector.
plots	Boolean to state if plots should be made of the outcome.

Value

List object containing variation explained, FMS, and degeneracy score metrics. If plots=TRUE, plots will be attached to the list.

Examples

set.seed(123)

I = 10
J = 5
K = 3
df = array(rnorm(I*J*K), c(I,J,K))
df2 = array(rnorm(I*J*K), c(I,J,K))
datasets = list(df, df2)
modes = list(c(1,2,3), c(1,4,5))

# A very small procedure is run to limit computational requirements
# Plots are not made to reduce CRAN runtime.
result = ACMTF_modelSelection(datasets,
                              modes,
                              maxNumComponents=1,
                              nstart=2,
                              cvFolds=2,
                              rel_tol=1e-1,
                              abs_tol=1e-1,
                              max_iter=2,
                              plots=FALSE)

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