Package: hNMF 1.0

hNMF: Hierarchical Non-Negative Matrix Factorization

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

Authors:Nicolas Sauwen

# Install 'hNMF' in R:

install.packages('hNMF', repos = c('https://nsauwen.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

Last updated 4 years agofrom:21f9b4ccbc. Checks:9 OK. Indexed: yes.

Help page	Topics
Accelerated hierarchical alternating least squares NMF. For a reference to the method, see N. Gillis, Nonnegative matrix factorization: complexity, algorithms and applications [Section 4.2, Algo. 6], PhD thesis, Université catholique de Louvain, February 2011.	HALSacc
Hierarchical non-negative matrix factorization.	hNMF
Overlay a mask or a color scaled image on top of a background image	imoverlay
Initialize NMF model with initial spectral data	initializeNMF
The successive projection algorithm, a useful method for initializing the NMF source matrix	initializeSPA
Perform Non-Negative Matrix factorization	oneLevelNMF
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
Condition input data matrix properly for NMF	preProcesInputData
Computation of relative NMF residual per observation	residualNMF
Apply fixed scaling to NMF model matrices by normalizing the basis vectors	scaleNMFResult
Semi-NMF based on multiplicative update rules. Reference: C. Ding, T. Li, and M.I. Jordan, "Convex and semi-nonnegative matrix factorizations", IEEE Transations on Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp. 45-55, 2010.	semiNMF