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nnmf (version 1.1)

Nonnegative Matrix Factorization

Description

Nonnegative matrix factorization (NMF) is a technique to factorize a matrix with nonnegative values into the product of two matrices. Covariates are also allowed. Parallel computing is an option to enhance the speed and high-dimensional and large scale (and/or sparse) data are allowed. Relevant papers include: Wang Y. X. and Zhang Y. J. (2012). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6), 1336-1353 and Kim H. and Park H. (2008). Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2), 713-730 .

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Version

Install

install.packages('nnmf')

Version

1.1

License

GPL (>= 2)

Maintainer

Michail Tsagris

Last Published

February 3rd, 2026

Functions in nnmf (1.1)

nmf.sqp

NMF minimizing the Frobenius norm
init

Initialization strategies for the NMF based on the k-means
nnmf-package

Nonnegative Matrix Factorization
nmf.manh

Simplicial NMF minimizing the Manhattan distance
nmfqp.reg

NMF with covariates minimizing the Frobenius norm
nmf.qp

NMF minimizing the Frobenius norm