These update rules, defined for the
NMFns-class
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_R
implements the same updates in
plain R.
Algorithms nsNMF and .R#nsNMF provide
the complete NMF algorithm from Pascual-Montano et
al. (2006), using the C++-optimised and plain R updates
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 = 2000, stopconv = 40, check.interval = 10) nmfAlgorithm.nsNMF(..., .stop = NULL, maxIter = 2000, copy = FALSE, stopconv = 40, check.interval = 10)
NMF-class
object.FALSE
) or
on a copy (TRUE
- default). With copy=FALSE
the memory footprint is very small, and some speed-up may
be achieved in the case of big matrices. However, greater
care should be taken due the side effect. We recommend
that only experienced users use copy=TRUE
.onInit
and Stop
respectively).maxIter
. nmf.stop.stationary
;
(object="NMFStrategy", i="integer", y="matrix",
x="NMF", ...)
, where object
is the
NMFStrategy
object that describes the algorithm
being run, i
is the current iteration, y
is
the target matrix and x
is the current value of
the NMF model. an NMFns-class
model object.
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.
The multiplicative updates are based on the updates
proposed by Brunet et al. (2004), except that the
NMF estimate W H
is replaced by W S H
and
W
(resp. H
) is replaced by W S
(resp.
S H
) in the update of H
(resp. W
).
See nmf_update.KL
for more details on the
update formula.
Pascual-Montano A, Carazo JM, Kochi K, Lehmann D and Pascual-marqui RD (2006). "Nonsmooth nonnegative matrix factorization (nsNMF)." _IEEE Trans. Pattern Anal. Mach. Intell_, *28*, pp. 403-415.
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,