tSNE: T-distributed Stochastic Neighbor Embedding (t-SNE)

In Mthrun/ProjectionBasedClustering: Projection Based Clustering

T-distributed Stochastic Neighbor Embedding (t-SNE)

Description

T-distributed Stochastic Neighbor Embedding res = tSNE(Data, KNN=30,OutputDimension=2)

Usage

tSNE(DataOrDistances,k,OutputDimension=2,Algorithm='tsne_cpp',

method="euclidean",Whitening=FALSE, Iterations=1000,PlotIt=FALSE,Cls,...)

Arguments

DataOrDistances	Numerical matrix defined as either Data, i.e., [1:n,1:d], nonsymmetric, and consists of n cases of d-dimensional data points with every case having d attributes, variables or features, or Distances, i.e.,[1:n,1:n], symmetric and consists of n cases, e.g., as.matrix(dist(Data,method))
k	number of k nearest neighbors=number of effective nearest neighbors("perplexity"); Important parameter. If not given, settings of packages of t-SNE will be used depending Algorithm
OutputDimension	Number of dimensions in the Outputspace, default=2
Algorithm	tsne_cpp': T-Distributed Stochastic Neighbor Embedding using a Barnes-HutImplementation in C++ of Rtsne 'tsne_r': pure R implementation of the t-SNE algorithm of of tsne
method	method specified by distance string: 'euclidean','cityblock=manhatten','cosine','chebychev','jaccard','minkowski','manhattan','binary'
Whitening	A boolean value indicating whether the matrix data should be whitened (tsne_r) or if pca should be used priorly (tsne_cpp)
Iterations	maximum number of iterations to perform.
PlotIt	Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown
Cls	[1:n,1] Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data.
...	Further arguments passed on to either 'Rtsne' or 'tsne'

Details

An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (doi: 10.1007/978-3-658-20540-9).

Value

List of

ProjectedPoints	[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projection
ModelObject	NULL for tsne_r, further information if tsne_cpp is selected

Note

A wrapper for Rtsne

or for tsne

You can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.

Author(s)

Michael Thrun

Examples

data('Hepta')
Data=Hepta$Data

Proj=tSNE(Data,k=7)

## Not run: 
PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls)

## End(Not run)

Mthrun/ProjectionBasedClustering documentation built on June 12, 2022, 1:12 p.m.