acmtfr_fg: Calculate function value of ACMTF

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

Calculate function value of ACMTF

Description

Calculate function value of ACMTF

Usage

acmtfr_fg(
  x,
  Z,
  Y,
  alpha = 1,
  beta = rep(0.001, length(Z$object)),
  epsilon = 1e-08,
  pi = 0.5,
  mu = 1e-06
)

Arguments

x	Vectorized parameters of the CMTF model.
Z	Z object as generated by setupCMTFdata().
Y	Dependent variable (regression part).
alpha	Alpha value of the loss function as specified by Acar et al., 2014
beta	Beta value of the loss function as specified by Acar et al., 2014
epsilon	Epsilon value of the loss function as specified by Acar et al., 2014
pi	Pi value of the loss function as specified by Van der Ploeg et al., 2025.
mu	Ridge term parameter for calculation of the regression coefficients rho (default = 1e-6).

Value

Scalar of the loss function value (when manual=FALSE), otherwise a list containing all loss terms.

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)
Y = A[,1]

init = initializeACMTF(Z, 2, output="vect")
outcome = acmtfr_fg(init, Z, Y)
f = outcome$fn
g = outcome$gr

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