ProjectionBasedClustering (version 1.2.1)

tSNE: 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,num_threads=1,...)

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

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. Requires Version >= 0.15 of Rtsne for multicore parallelisation.

'tsne_opt_cpp': T-Distributed Stochastic Neighbor Embedding with automated optimized parameters using a Barnes-HutImplementation in C++ of [Ulyanov, 2016].

'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.

num_threads

Number of threads for parallel computation, only usable for Algorithm='tsne_cpp' or 'tsne_opt_cpp'

...

Further arguments passed on to either 'Rtsne' or 'tsne'

Author

Michael Thrun, Luca Brinkmann

Details

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

References

Anna C. Belkina, Christopher O. Ciccolella, Rina Anno, Josef Spidlen, Richard Halpert, Jennifer Snyder-Cappione: Automated optimal parameters for T-distributed stochastic neighbor embedding improve visualization and allow analysis of large datasets, bioRxiv 451690, doi: https://doi.org/10.1101/451690, 2018.

L.J.P van der Maaten: Accelerating t-SNE using tree-based algorithms, Journal of Machine Learning Research 15.1:3221-3245, 2014.

Ulyanov, Dmitry: Multicore-TSNE, GitHub repository URL https://github.com/DmitryUlyanov/Multicore-TSNE, 2016.

Examples

data('Hepta')
Data=Hepta$Data

if (FALSE) {
Proj=tSNE(Data,k=7)

PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls)
}
# \dontshow{
Proj=tSNE(Data[1:10,],k = 2,Algorithm="tsne_r")
# }