tSNE: T-distributed Stochastic Neighbor Embedding

Description Usage Arguments Value Note Author(s)

Description

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

Usage

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tSNE(DataOrDists,k,OutputDimension=2,method="euclidean",Whitening=TRUE,

InitialDimensions=NULL, Iterations=1000,PlotIt=FALSE,Cls)

Arguments

DataOrDists

array of data: n cases in rows, d variables in columns, matrix is not symmetric or distance matrix, in this case matrix has to be symmetric

k

number of k nearest neighbors=number of effective nearest neighbors("perplexity") Important parameter, if not given Settings of package t-SNE will be used

OutputDimension

Number of dimensions in the Outputspace, default=2

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

InitialDimensions

The number of dimensions to use in reduction method.

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.

Value

ProjectedPoints[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projection

Note

A wrapper for tsne

Author(s)

Michael Thrun


Mthrun/ProjectionBasedClustering documentation built on May 29, 2019, 10:53 a.m.