PGNMF

Input data matrix, each column represents one data point 
and the rows correspond to the different features

Valid NMF model, containing initialized factor matrices
(in accordance with the NMF package definition)

nmfMod

Tolerance for a relative stopping condition

maxIter

Limit of time duration NMF analysis

timeLimit

Boolean indicating whether divergence checking should be performed
Default is TRUE, but it should be set to FALSE when using random initialization

checkDivergence

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.

Hierarchical and single-level non-negative matrix factorization. Several NMF algorithms are available.

Nicolas Sauwen

hNMF

Hierarchical Non-Negative Matrix Factorization

PGNMF function

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. — PGNMF

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.

PGNMF: 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.

Description

Usage

Arguments

Value