d_SVD: Singular Value Decomposition

In egenn/rtemis: Machine Learning and Visualization

Singular Value Decomposition

Description

Perform SVD decomposition using base::svd

Usage

d_SVD(
  x,
  x.test = NULL,
  k = 2,
  nu = 0,
  scale = TRUE,
  center = TRUE,
  verbose = TRUE,
  ...
)

Arguments

x	Input matrix
x.test	Optional test set matrix. Will be projected on to SVD bases
k	Integer: Number of right singular vectors to compute (svd's nv)
nu	Integer: Number of left singular vectors to compute
scale	Logical: If TRUE, scale input data before doing SVD. Default = TRUE
center	Logical: If TRUE, also center input data if scale is TRUE. Default = TRUE
verbose	Logical: If TRUE, print messages to screen. Default = TRUE
...	Additional parameters to be passed to svd

Details

Same solution as d_PCA

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_NMF(), d_PCA(), d_SPCA(), d_TSNE(), d_UMAP()

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