NMF-package {NMF}R Documentation

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.

Author(s)

Renaud Gaujoux renaud@cbio.uct.ac.za

References

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
## Not run: metaheatmap(res) # mixture coefficients
## Not run: 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
## Not run: metaheatmap(res) # mixture coefficients
## Not run: metaheatmap(res, 'features') # basis vectors


[Package NMF version 0.2.4 Index]