Non-Negative Matrix Factorization
S4 Class implementing NNMF.
NNMF is a method for decomposing a matrix into a smaller dimension such that the constraint that the data (and the projection) are not negative is taken into account.
funA function that does the embedding and returns a dimRedResult object.
stdparsThe standard parameters for the function.
Dimensionality reduction methods are S4 Classes that either be used
directly, in which case they have to be initialized and a full
list with parameters has to be handed to the @fun()
slot, or the method name be passed to the embed function and
parameters can be given to the ..., in which case
missing parameters will be replaced by the ones in the
@stdpars.
The method can take the following parameters:
The number of output dimensions.
character, which algorithm should be used. See
nmf for possible values. Defaults to
"brunet"
integer, the number of times the computations are
conducted. See nmf
integer, a value to control the random numbers used.
named list, other options to pass to nmf
Wraps around nmf. Note that the estimation uses random
numbers. To create reproducible results, set the random number seed in the
function call. Also, in many cases, the computations will be conducted
in parallel using multiple cores. To disable this, use the option
.pbackend = NULL.
Lee, D.D., Seung, H.S., 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788-791. https://doi.org/10.1038/44565
Other dimensionality reduction methods: AutoEncoder-class,
DRR-class,
DiffusionMaps-class,
DrL-class, FastICA-class,
FruchtermanReingold-class,
HLLE-class, Isomap-class,
KamadaKawai-class, LLE-class,
MDS-class, PCA-class,
PCA_L1-class, UMAP-class,
dimRedMethod-class,
dimRedMethodList, kPCA-class,
nMDS-class, tSNE-class
dat <- loadDataSet("Iris")
set.seed(4646)
factorization <- embed(dat, "NNMF")
proj_dat <- factorization@apply(dat)
plot(proj_dat@data[, 1], proj_dat@data[, 2])
# project new values:
nn_proj <- predict(factorization, iris[1:7, 1:4])
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