d_TSNE: t-distributed Stochastic Neighbor Embedding

In egenn/rtemis: Machine Learning and Visualization

t-distributed Stochastic Neighbor Embedding

Description

Perform t-SNE decomposition using Rtsne::Rtsne

Usage

d_TSNE(
  x,
  k = 3,
  initial.dims = 50,
  perplexity = 15,
  theta = 0,
  check.duplicates = TRUE,
  pca = TRUE,
  max.iter = 1000,
  scale = FALSE,
  center = FALSE,
  is.distance = FALSE,
  verbose = TRUE,
  outdir = "./",
  ...
)

Arguments

x	Input matrix
k	Integer. Number of t-SNE components required
initial.dims	Integer: Number of dimensions to retain in initial PCA. Default = 50
perplexity	Numeric: Perplexity parameter
theta	Float: 0.0: exact TSNE. Increase for higher speed, lower accuracy. Default = 0
check.duplicates	Logical: If TRUE, Checks whether duplicates are present. Best to set test manually
pca	Logical: If TRUE, perform initial PCA step. Default = TRUE
max.iter	Integer: Maximum number of iterations. Default = 1000
scale	Logical: If TRUE, scale before running t-SNE using base::scale. Default = FALSE
center	Logical: If TRUE, and scale = TRUE, also center. Default = FALSE
is.distance	Logical: If TRUE, x should be a distance matrix. Default = FALSE
verbose	Logical: If TRUE, print messages to output
outdir	Path to output directory
...	Options for Rtsne::Rtsne

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_SVD(), d_UMAP()

egenn/rtemis documentation built on May 4, 2024, 7:40 p.m.