Description Usage Arguments Details Value Author(s) References
These update rules proposed by Badea (2008) are modified version of the updates from Lee et al. (2001), that include an offset/intercept vector, which models a common baseline for each feature accross all samples:
V \approx W H + I
nmf_update.euclidean_offset.h
and nmf_update.euclidean_offset.w
compute the updated NMFOffset model, using the optimized C++ implementations.
nmf_update.offset_R
implements a complete single update step,
using plain R updates.
nmf_update.offset
implements a complete single update step,
using C++optimised updates.
Algorithms ‘offset’ and ‘.R#offset’ provide the complete NMFwithoffset algorithm
from Badea (2008), using the C++optimised and pure R updates nmf_update.offset
and nmf_update.offset_R
respectively.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  nmf_update.euclidean_offset.h(v, w, h, offset,
eps = 10^9, copy = TRUE)
nmf_update.euclidean_offset.w(v, w, h, offset,
eps = 10^9, copy = TRUE)
nmf_update.offset_R(i, v, x, eps = 10^9, ...)
nmf_update.offset(i, v, x, copy = FALSE, eps = 10^9,
...)
nmfAlgorithm.offset_R(..., .stop = NULL,
maxIter = nmf.getOption("maxIter") %% 2000,
eps = 10^9, stopconv = 40, check.interval = 10)
nmfAlgorithm.offset(..., .stop = NULL,
maxIter = nmf.getOption("maxIter") %% 2000,
copy = FALSE, eps = 10^9, stopconv = 40,
check.interval = 10)

offset 
current value of the offset/intercept vector. It must be of length equal to the number of rows in the target matrix. 
v 
target matrix. 
eps 
small numeric value used to ensure numeric stability, by shifting up entries from zero to this fixed value. 
copy 
logical that indicates if the update should be made on the original
matrix directly ( 
i 
current iteration number. 
x 
current NMF model, as an 
... 
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 
.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. 
w 
current basis matrix 
h 
current coefficient matrix 
The associated model is defined as an NMFOffset
object.
The details of the multiplicative updates can be found in Badea (2008).
Note that the updates are the ones defined for a single datasets, not the
simultaneous NMF model, which is fit by algorithm ‘siNMF’ from
formulabased NMF models.
an NMFOffset
model object.
Original update definition: Liviu Badea
Port to R and optimisation in C++: Renaud Gaujoux
Badea L (2008). "Extracting gene expression profiles common to colon and pancreatic adenocarcinoma using simultaneous nonnegative matrix factorization." _Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing_, *290*, pp. 26778. ISSN 17935091, <URL: http://www.ncbi.nlm.nih.gov/pubmed/18229692>.
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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