# tsne: t-SNE Embedding In maotai: Tools for Matrix Algebra, Optimization and Inference

## Description

This function is a simple wrapper of Rtsne function for t-Stochastic Neighbor Embedding for finding low-dimensional structure of the data embedded in the high-dimensional space.

## Usage

 1 tsne(data, ndim = 2, ...) 

## Arguments

 data an (n\times p) matrix whose rows are observations. ndim an integer-valued target dimension. ... extra parameters to be used in Rtsne function.

## Value

a named list containing

embed

an (n\times ndim) matrix whose rows are embedded observations.

stress

discrepancy between embedded and origianl data as a measure of error.

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 ## use simple example of iris dataset data(iris) mydat = as.matrix(iris[,1:4]) mylab = as.factor(iris[,5]) ## run t-SNE and MDS for comparison iris.cmds = cmds(mydat, ndim=2) iris.tsne = tsne(mydat, ndim=2) ## extract coordinates and class information cx = iris.cmds$embed # embedded coordinates of CMDS tx = iris.tsne$embed # t-SNE ## visualize # main title mc = paste("CMDS with STRESS=",round(iris.cmds$stress,4),sep="") mt = paste("tSNE with STRESS=",round(iris.tsne$stress,4),sep="") # draw a figure opar <- par(no.readonly=TRUE) par(mfrow=c(1,2)) plot(cx, col=mylab, pch=19, main=mc) plot(tx, col=mylab, pch=19, main=mt) par(opar) 

maotai documentation built on Feb. 3, 2022, 5:09 p.m.