NMFns
model $V \approx W S H$
from Pascual-Montano et al. (2006), that
introduces an intermediate smoothing matrix to enhance
sparsity of the factors. nmf_update.ns
computes the updated nsNMF model. It
uses the optimized C++ implementations
nmf_update.KL.w
and
nmf_update.KL.h
to update $W$ and
$H$ respectively.
nmf_update.ns_R
implements the same updates in
plain R.
Algorithms nmf_update.brunet
and
nmf_update.brunet_R
respectively. The
stopping criterion is based on the stationarity of the
connectivity matrix.
nmf_update.ns(i, v, x, copy = FALSE, ...) nmf_update.ns_R(i, v, x, ...)
nmfAlgorithm.nsNMF_R(..., .stop = NULL,
maxIter = nmf.getOption("maxIter") %||% 2000,
stopconv = 40, check.interval = 10)
nmfAlgorithm.nsNMF(..., .stop = NULL,
maxIter = nmf.getOption("maxIter") %||% 2000,
copy = FALSE, stopconv = 40, check.interval = 10)
NMF
object.FALSE
) or
on a copy (TRUE
- default). With copy=FALSE
the memory footprint is very small, and some speed-up may
be achievedonInit
and Stop
respectively).NMFns
model object. See nmf_update.KL
for more details on the
update formula.
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.
4164-9. ISSN 0027-8424,