volnmf_det: Update volume-regularized matrix 'R' using det volume...

In vrnmf: Volume-Regularized Structured Matrix Factorization

Update volume-regularized matrix R using det volume approximation

Description

volnmf_det finds matrix R that minimizes objective ||X-C*R||^2 + w.vol*det(R)

Usage

volnmf_det(
  C,
  X,
  R,
  posit = FALSE,
  w.vol = 0.1,
  eigen.cut = 1e-16,
  err.cut = 0.001,
  n.iter = 1000
)

Arguments

C	Numeric Matrices. Matrices involved in objective function. Matrix R serves as initialization.
X	Numeric Matrices. Matrices involved in objective function. Matrix R serves as initialization.
R	Numeric Matrices. Matrices involved in objective function. Matrix R serves as initialization.
posit	A boolean. Set up (TRUE) or not (FALSE) non-negative constraints on matrix R. (default=TRUE)
w.vol	A numeric. Volume (det) weight in objective function. (default=0.1)
eigen.cut	A numeric. Threshold on eigenvalue of SVD eigenvectors. (default=1e-16)
err.cut	A numeric. Stop algorithm if relative erro in R between iteration is less than err.cut. (default=1e-3)
n.iter	An integer. Number of iterations. (default=1e+3)

Value

An updated matrix R.

vrnmf documentation built on March 18, 2022, 6:11 p.m.