acmtfr_gradient: Calculate gradient of ACMTF model.

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

Calculate gradient of ACMTF model.

Description

Calculate gradient of ACMTF model.

Usage

acmtfr_gradient(
  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

Vectorized gradient of the ACMTF regression model.

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")
g = acmtfr_gradient(init, Z, Y)

CMTFtoolbox documentation built on Aug. 29, 2026, 1:07 a.m.