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Weighted CP Decomposition via Direct Optimization

Source: R/cp_variants.R
cp_wopt.Rd

Fits a CP model to data with a weight (indicator) tensor by minimizing ||W * (X - K)||^2, mirroring the MATLAB Tensor Toolbox cp_wopt. Use a 0/1 weight tensor to fit in the presence of missing entries.

Usage

cp_wopt(
  X,
  W,
  R,
  init = "random",
  maxiters = 500L,
  factr = 1e+07,
  printitn = 0L
)

Arguments

X

A Tensor or array-like object (missing entries may hold any value, typically 0).

W

A weight tensor of the same dimensions as X (commonly 0/1).

R

Target CP rank.

init

"random" or a list of initial factor matrices.

maxiters

Maximum optimizer iterations (default 500).

factr

optim L-BFGS-B factr convergence parameter.

printitn

If positive, print the optimizer trace.

Value

A KTensor.

Examples

set.seed(1)
X <- tensor(array(rnorm(24), dim = c(2, 3, 4)))
W <- tensor(array(rbinom(24, 1, 0.8), dim = c(2, 3, 4)))
K <- cp_wopt(X, W, R = 2)