Description Usage Arguments Details Author(s) References
The built-in 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 and Seung (2001).
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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | 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
)
|
i |
current iteration number. |
v |
target matrix. |
x |
current NMF model, as an |
rescale |
logical that indicates if the basis matrix W should be rescaled so that its columns sum up to one. |
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 ( |
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 |
.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.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, 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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