Description Usage Arguments Details Author(s) References
The builtin NMF algorithms described here minimise the Frobenius norm (Euclidean distance) between an NMF model and a target matrix. They use the updates for the basis and coefficient matrices (W and H) defined by Lee et al. (2001).
nmf_update.lee
implements in C++ an optimised
version of the single update step.
Algorithms ‘lee’ and ‘.R#lee’ provide the
complete NMF algorithm from Lee et al. (2001),
using the C++optimised and pure R updates
nmf_update.lee
and
nmf_update.lee_R
respectively.
Algorithm ‘Frobenius’ provides an NMF algorithm
based on the C++optimised version of the updates from
Lee et al. (2001), which uses the stationarity of
the objective value as a stopping criterion
nmf.stop.stationary
, instead of the
stationarity of the connectivity matrix
nmf.stop.connectivity
as used by
‘lee’.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  nmf_update.lee_R(i, v, x, rescale = TRUE, eps = 10^9,
...)
nmf_update.lee(i, v, x, rescale = TRUE, copy = FALSE,
eps = 10^9, weight = NULL, ...)
nmfAlgorithm.lee_R(..., .stop = NULL,
maxIter = nmf.getOption("maxIter") %% 2000,
rescale = TRUE, eps = 10^9, stopconv = 40,
check.interval = 10)
nmfAlgorithm.lee(..., .stop = NULL,
maxIter = nmf.getOption("maxIter") %% 2000,
rescale = TRUE, copy = FALSE, eps = 10^9,
weight = NULL, stopconv = 40, check.interval = 10)
nmfAlgorithm.Frobenius(..., .stop = NULL,
maxIter = nmf.getOption("maxIter") %% 2000,
rescale = TRUE, copy = FALSE, eps = 10^9,
weight = NULL, stationary.th = .Machine$double.eps,
check.interval = 5 * check.niter, check.niter = 10L)

rescale 
logical that indicates if the basis matrix W should be rescaled so that its columns sum up to one. 
i 
current iteration number. 
v 
target matrix. 
x 
current NMF model, as an

eps 
small numeric value used to ensure numeric stability, by shifting up entries from zero to this fixed value. 
... 
extra arguments. These are generally not used
and present only to allow other arguments from the main
call to be passed to the initialisation and stopping
criterion functions (slots 
copy 
logical that indicates if the update should
be made on the original matrix directly ( 
.stop 
specification of a stopping criterion, that is used instead of the one associated to the NMF algorithm. It may be specified as:

maxIter 
maximum number of iterations to perform. 
stopconv 
number of iterations intervals over which the connectivity matrix must not change for stationarity to be achieved. 
check.interval 
interval (in number of iterations) on which the stopping criterion is computed. 
stationary.th 
maximum absolute value of the gradient, for the objective function to be considered stationary. 
check.niter 
number of successive iteration used to compute the stationnary criterion. 
weight 
numeric vector of sample weights, e.g.,
used to normalise samples coming from multiple datasets.
It must be of the same length as the number of
samples/columns in 
nmf_update.lee_R
implements in pure R a single
update step, i.e. it updates both matrices.
Original update definition: D D Lee and HS Seung
Port to R and optimisation in C++: Renaud Gaujoux
Lee DD and Seung H (2001). "Algorithms for nonnegative matrix factorization." _Advances in neural information processing systems_. <URL: http://scholar.google.com/scholar?q=intitle:Algorithms+for+nonnegative+matrix+factorization\#0>.
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