nmf_update.brunet_R  R Documentation 
The builtin NMF algorithms described here minimise the
KullbackLeibler divergence (KL) between an NMF model and
a target matrix. They use the updates for the basis and
coefficient matrices (W
and H
) defined by
Brunet et al. (2004), which are essentially those
from Lee et al. (2001), with an stabilisation step
that shift up all entries from zero every 10 iterations,
to a very small positive value.
nmf_update.brunet
implements in C++ an optimised
version of the single update step.
Algorithms ‘brunet’ and ‘.R#brunet’ provide
the complete NMF algorithm from Brunet et al.
(2004), using the C++optimised and pure R updates
nmf_update.brunet
and
nmf_update.brunet_R
respectively.
Algorithm ‘KL’ provides an NMF algorithm based on
the C++optimised version of the updates from
Brunet et al. (2004), 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
‘brunet’.
nmf_update.brunet_R(i, v, x, eps = .Machine$double.eps,
...)
nmf_update.brunet(i, v, x, copy = FALSE,
eps = .Machine$double.eps, ...)
nmfAlgorithm.brunet_R(..., .stop = NULL,
maxIter = nmf.getOption("maxIter") %% 2000,
eps = .Machine$double.eps, stopconv = 40,
check.interval = 10)
nmfAlgorithm.brunet(..., .stop = NULL,
maxIter = nmf.getOption("maxIter") %% 2000,
copy = FALSE, eps = .Machine$double.eps, stopconv = 40,
check.interval = 10)
nmfAlgorithm.KL(..., .stop = NULL,
maxIter = nmf.getOption("maxIter") %% 2000,
copy = FALSE, eps = .Machine$double.eps,
stationary.th = .Machine$double.eps,
check.interval = 5 * check.niter, check.niter = 10L)
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. 
nmf_update.brunet_R
implements in pure R a single
update step, i.e. it updates both matrices.
Original implementation in MATLAB: JeanPhilippe Brunet brunet@broad.mit.edu
Port to R and optimisation in C++: Renaud Gaujoux
Original license terms:
This software and its documentation are copyright 2004 by the Broad Institute/Massachusetts Institute of Technology. All rights are reserved. This software is supplied without any warranty or guaranteed support whatsoever. Neither the Broad Institute nor MIT can not be responsible for its use, misuse, or functionality.
Brunet J, Tamayo P, Golub TR and Mesirov JP (2004). "Metagenes and molecular pattern discovery using matrix factorization." _Proceedings of the National Academy of Sciences of the United States of America_, *101*(12), pp. 41649. ISSN 00278424, <URL: http://dx.doi.org/10.1073/pnas.0308531101>, <URL: http://www.ncbi.nlm.nih.gov/pubmed/15016911>.
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>.
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