volnmf_det {vrnmf}

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.