PGNMF: NMF by alternating non-negative least squares using projected...

In hNMF: Hierarchical Non-Negative Matrix Factorization

Description

NMF by alternating non-negative least squares using projected gradients. For a reference to the method, see C.-J. Lin, "Projected Gradient Methods for Non-negative Matrix Factorization", Neural computation 19.10 (2007): 2756-2779.

Usage

PGNMF(X, nmfMod, tol = 1e-05, maxIter = 500, timeLimit = 300,
  checkDivergence = TRUE)

Arguments

X	Input data matrix, each column represents one data point and the rows correspond to the different features
nmfMod	Valid NMF model, containing initialized factor matrices (in accordance with the NMF package definition)
tol	Tolerance for a relative stopping condition
maxIter	Maximum number of iterations
timeLimit	Limit of time duration NMF analysis
checkDivergence	Boolean indicating whether divergence checking should be performed Default is TRUE, but it should be set to FALSE when using random initialization

Value

Resulting NMF model (in accordance with the NMF package definition)

Author(s)

nsauwen

hNMF documentation built on Jan. 8, 2021, 5:42 p.m.