nonlinear_TSNE: t-distributed Stochastic Neighbor Embedding
In Rdimtools: Dimension Reduction and Estimation Methods

do.tsne

R Documentation

t-distributed Stochastic Neighbor Embedding

Description

t-distributed Stochastic Neighbor Embedding (t-SNE) is a variant of Stochastic Neighbor Embedding (SNE) that mimicks patterns of probability distributinos over pairs of high-dimensional objects on low-dimesional target embedding space by minimizing Kullback-Leibler divergence. While conventional SNE uses gaussian distributions to measure similarity, t-SNE, as its name suggests, exploits a heavy-tailed Student t-distribution.

Usage

do.tsne(
  X,
  ndim = 2,
  perplexity = 30,
  eta = 0.05,
  maxiter = 2000,
  jitter = 0.3,
  jitterdecay = 0.99,
  momentum = 0.5,
  pca = TRUE,
  pcascale = FALSE,
  symmetric = FALSE,
  BHuse = TRUE,
  BHtheta = 0.25
)

Arguments

`X`	an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables.
`ndim`	an integer-valued target dimension.
`perplexity`	desired level of perplexity; ranging [5,50].
`eta`	learning parameter.
`maxiter`	maximum number of iterations.
`jitter`	level of white noise added at the beginning.
`jitterdecay`	decay parameter in (0,1). The closer to 0, the faster artificial noise decays.
`momentum`	level of acceleration in learning.
`pca`	whether to use PCA as preliminary step; `TRUE` for using it, `FALSE` otherwise.
`pcascale`	a logical; `FALSE` for using Covariance, `TRUE` for using Correlation matrix. See also `do.pca` for more details.
`symmetric`	a logical; `FALSE` to solve it naively, and `TRUE` to adopt symmetrization scheme.
`BHuse`	a logical; `TRUE` to use Barnes-Hut approximation. See `Rtsne` for more details.
`BHtheta`	speed-accuracy tradeoff. If set as 0.0, it reduces to exact t-SNE.

Value

a named Rdimtools S3 object containing

Y: an (n\times ndim) matrix whose rows are embedded observations.
algorithm: name of the algorithm.

Author(s)

Kisung You

References

\insertRef

vandermaaten_visualizing_2008Rdimtools

Examples


## load iris data
data(iris)
set.seed(100)
subid = sample(1:150,50)
X     = as.matrix(iris[subid,1:4])
lab   = as.factor(iris[subid,5])

## compare different perplexity
out1 <- do.tsne(X, ndim=2, perplexity=5)
out2 <- do.tsne(X, ndim=2, perplexity=10)
out3 <- do.tsne(X, ndim=2, perplexity=15)

## Visualize three different projections
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=lab, main="tSNE::perplexity=5")
plot(out2$Y, pch=19, col=lab, main="tSNE::perplexity=10")
plot(out3$Y, pch=19, col=lab, main="tSNE::perplexity=15")
par(opar)

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.