nsNMF-nmf: NMF Multiplicative Update for Nonsmooth Nonnegative Matrix...
In renozao/NMF: Algorithms and Framework for Nonnegative Matrix Factorization (NMF)

Description Usage Arguments Details Value References

These update rules, defined for the 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(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
)

`i`	current iteration number.
`v`	target matrix.
`x`	current NMF model, as an `NMF` object.
`copy`	logical that indicates if the update should be made on the original matrix directly (`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`.
`...`	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 `onInit` and `Stop` respectively).
`.stop`	specification of a stopping criterion, that is used instead of the one associated to the NMF algorithm. It may be specified as: the access key of a registered stopping criterion; a single integer that specifies the exact number of iterations to perform, which will be honoured unless a lower value is explicitly passed in argument `maxIter`. a single numeric value that specifies the stationnarity threshold for the objective function, used in with `nmf.stop.stationary`; a function with signature `(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.
`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.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.

an NMFns model object.

Pascual-Montano A, Carazo JM, Kochi K, Lehmann D, Pascual-marqui RD (2006). “Nonsmooth nonnegative matrix factorization (nsNMF).” _IEEE Trans. Pattern Anal. Mach. Intell_, *28*, 403-415.

Brunet J, Tamayo P, Golub TR, 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), 4164-9. ISSN 0027-8424, doi: 10.1073/pnas.0308531101 (URL: https://doi.org/10.1073/pnas.0308531101).

renozao/NMF documentation built on June 14, 2020, 9:35 p.m.