NMF (version 0.5.06)

NMF-package: NMF Package Overview

Description

The NMF package provides methods to perform Nonnegative Matrix Factorization (NMF) , as well as a framework to develop and test new NMF algorithms.

A number of standard algorithms and seeding methods are implemented. Tuned visualisation and post-analysis methods help in the evaluation of the algorithms' performances or in the interpretation of the results.

References

If you use the 'NMF' package in publications cite:

Renaud Gaujoux, Cathal Seoighe (2010). A flexible R package for nonnegative matrix factorization. BMC Bioinformatics 2010, 11:367 http://www.biomedcentral.com/1471-2105/11/367

For pointers on further readings, please see references therein.

Definition of Nonnegative Matrix Factorization in its modern formulation: Lee D.D. and Seung H.S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788--791.

Historical first definition and algorithms: Paatero, P., Tapper, U. (1994). Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics, 2, 111--126 , doi:10.1002/env.3170050203.

See Also

NMF-class, nmf, Biobase

Examples

# run default NMF algorithm on a random matrix
V <- matrix(runif(10000), 500, 20)
res <- nmf(V, 3)
res

# compute some quality measures
summary(res)

# Visualize the results as heatmaps
metaheatmap(res) # mixture coefficients
metaheatmap(res, 'features') # basis vectors

# run default NMF algorithm on a random matrix with actual patterns
set.seed(123456)
V <- syntheticNMF(500, 3, 20, noise=TRUE)
res <- nmf(V, 3)
res

# compute some quality measures
summary(res)

# Visualize the results as heatmaps
metaheatmap(res) # mixture coefficients
metaheatmap(res, 'features') # basis vectors