d_NMF: Non-negative Matrix Factorization (NMF)

In egenn/rtemis: Machine Learning and Visualization

Non-negative Matrix Factorization (NMF)

Description

Perform NMF decomposition using NMF::nmf

Usage

d_NMF(
  x,
  x.test = NULL,
  k = 2,
  method = "brunet",
  nrun = 30,
  scale = TRUE,
  center = FALSE,
  verbose = TRUE,
  ...
)

Arguments

x	Input data
x.test	Optional test set. Will be projected on to NMF basis
k	Integer vector of length 1 or greater. Rank of decomposition
method	NMF method. Defaults to "brunet". See NMF::nmf
nrun	Integer: Number of runs to perform
scale	Logical: If TRUE, scale input data before projecting
center	Logical: If TRUE, also center input data if scale is TRUE
verbose	Logical: If TRUE, print messages to screen. Default = TRUE
...	Additional parameters to be passed to NMF::nmf

Details

Project scaled variables to NMF bases. Input must be n by p, where n represents number of cases, and p represents number of features. NMF will be applied to the transpose of the n x p matrix.

Value

rtDecom object

Author(s)

E.D. Gennatas

See Also

Other Decomposition: d_H2OAE(), d_H2OGLRM(), d_ICA(), d_Isomap(), d_KPCA(), d_LLE(), d_MDS(), d_PCA(), d_SPCA(), d_SVD(), d_TSNE(), d_UMAP()

egenn/rtemis documentation built on Oct. 28, 2024, 6:30 a.m.