Description Usage Arguments Details Value Author(s) References
These update rules proposed by Badea (2008) are modified version of the updates from Lee and Seung (2001), that include an offset/intercept vector, which models a common baseline for each feature accross all samples:
V \approx W H + I
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | 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
)
|
v |
target matrix. |
w |
current basis matrix |
h |
current coefficient matrix |
offset |
current value of the offset/intercept vector. It must be of length equal to the number of rows in the 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. |
nmf_update.euclidean_offset.h
and nmf_update.euclidean_offset.w
compute the updated NMFOffset model, using the optimized C++ implementations.
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
formula-based 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*, 267-78. ISSN 1793-5091, <URL: http://www.ncbi.nlm.nih.gov/pubmed/18229692>.
Lee DD, Seung H (2001). “Algorithms for non-negative matrix factorization.” _Advances in neural information processing systems_. <URL: http://scholar.google.com/scholar?q=intitle:Algorithms+for+non-negative+matrix+factorization\#0>.
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